International Conference on Statistical Methods for Analyzing Engineering Data

NEXT EVENT SESSION
23-24 June 2023 (Instant E-Certificate)
For Enquiries:
statistics@researchw.com

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About the Conference

Introduction of the conferences

International Conference on Statistical Methods for Analyzing Engineering Data is an interdisciplinary conference that brings together experts from engineering, statistics, and related fields to exchange ideas, present new research findings, and discuss emerging trends in statistical methods for engineering data analysis.The conference covers a wide range of topics, including experimental design, statistical process control, reliability analysis, multivariate analysis, time series analysis, and data mining. It also includes sessions on the use of statistical methods in specific engineering applications such as environmental monitoring, quality control, and product design.The conference features keynote speakers who are leaders in the field, as well as contributed paper sessions and poster presentations. It provides a platform for researchers, practitioners, and educators to network, collaborate, and share their expertise. The conference proceedings are published in a high-quality scientific journal or proceedings volume, ensuring that the research presented at the conference reaches a wider audience.

Theme: The theme of the International Conference on Statistical Methods for Analyzing Engineering Data can vary depending on the specific conference in question.

Theme

Theme

The theme of the International Conference on Statistical Methods for Analyzing Engineering Data can vary depending on the specific conference in question.

Objectives

Objectives

The objectives of the International Conference on Statistical Methods for Analyzing Engineering Data can vary, but some common objectives include:

  1. To provide a platform for researchers, scientists, engineers, and industry professionals to exchange ideas and knowledge on the latest developments in the field of Statistical Methods for Analyzing Engineering Data.
  2. To promote interdisciplinary collaboration and encourage cross-fertilization of ideas across different fields and industries, including Statistical, Methods, Analyzing, Data, and engineering.
  3. To showcase the latest research findings and technological advancements in the field of Statistical Methods for Analyzing Engineering Data, and to provide an opportunity for researchers to present their work to a global audience.
  4. To facilitate the transfer of knowledge and technology from academia to industry and to encourage the commercialization of new Statistical Methods for Analyzing Engineering Data-based products.
  5. To foster the development of new partnerships and collaborations between researchers, industry, and government, with the goal of promoting the growth and development of the field of Statistical Methods for Analyzing Engineering Data
  6. To provide a forum for discussing the ethical, social, and environmental implications of Statistical Methods for Analyzing Engineering Data and to encourage responsible and sustainable research and development in the field.

These objectives provide a framework for the International Conference on Statistical Methods for Analyzing Engineering Data and are designed to advance the field by promoting collaboration, exchange of ideas, and the dissemination of new knowledge and technology.

Organizers

Organizers

Science Father is a international conferences  organizer and publish the videos, books and news in various themes of scientific research. Articles Presented in our conference are Peer Reviewed. We build the perfect environment for learning, sharing, networking and Awarding via Academic conferences, workshops, symposiums, seminars, awards and other events. We establish our Relationship with the scholars and the Universities through various activities such as seminars, workshops, conferences and Symposia. We are a decisive, conclusive & fast-moving company open to new ideas and ingenious publishing. We also preserve the long-term relationships with our authors and supporting them throughout their careers. We acquire, develop and distribute knowledge by disseminating scholarly and professional materials around the world. All  conference and award presentations are maintain the highest standards of quality, with Editorial Boards composed of scholars & Experts from around the world.

Dates and Location

Dates and Location

International Conference on Statistical Methods for Analyzing Engineering Data, organized by ScienceFather group

4th Edition of  Statistical Methods for Analyzing Engineering Data | 23-24 June 2023 |San Francisco, United States (Hybrid)

Call for paper

Call for Abstract/paper

Original Articles/papers are invited from Industry Persons, Scientist, Academician, Research Scholars, P.G. & U.G. Students for presentation in our International Conference. All articles/papers must be in MS-Word (.doc or .docx) format, including the title, author's name, an affiliation of all authors, e-mail, abstract, keywords, Conclusion, Acknowledgment, and References.

Submit Abstract

The Candidates with eligibility can click the "Submit Paper/Abstract Now" button and fill up the online submission form and Submit.

Abstract/Full Paper submission

Final/Full Paper submission is optional: If you don't want your abstract/full paper to be published in the Conference Abstracts & Proceedings CD (with ISBN number) and only want to present it at the conference, it is acceptable.

Page limit: There is a limit of 6-8 pages for a final/full paper. An additional page is chargeable.

Paper language: Final/Full papers should be in English.

Templates: "Final paper template," "Final abstract template"

All the final papers should be uploaded to the website online system according to "The final paper template" as word doc. Or Docx, since this will be the camera-ready published version. Please note that final papers that are not uploaded to online System as a word doc./docx after the opening of final paper submissions according to the template above will not be published in the CONFERENCE Abstracts & Proceedings CD (with ISBN)

Journal Publication

Journal Publication

Statistical Methods Conferences All accepted papers will be included in the conference proceedings, which will be recommended in one of the author's prescribed ScienceFather International journals.

Registration

Registration Procedure

  • Click the “Register Now” button on the conference page and enter your Submission ID in the Search Box
  • Your Submissions will be listed on that page. You can find the Register Now link beside your submission. Click the link, and now you will be redirected to the Conference registration form where you can make your registration using credit/debit cards.
  • The Fee charged for E-Poster is to display the E-Posters only on the Website. The Abstract will be published in the conference proceeding book.

Registration Types

Speaker Registration

  • Access to all event Session
  • Certificate of Presentation
  • Handbook
  • Conference Kit
  • Tea, Coffee & Snack,
  • Lunch during the Conference
  • Publication of Abstract /Full Paper at the Conference Proceedings Book
  • Opportunity to give a Keynote/ Poster Presentations/ Plenary/ Workshop
  • Opportunity to publish your Abstract in any of our esteemed Journals discounted rate
  • Opportunity to publish your full article in our open access book at a discounted rate
  • One to One Expert Forums

Delegate (Participant) Registration

  • Access to all Event Sessions
  • Participation Certificate
  • Handbook
  • Conference Kit
  • Tea, Coffee & Snack,
  • Lunch during the Conference
  • Delegates are not allowed to present

Poster Registration

  • Includes all the above Registration Benefits
  • You will have to bring your Posters to the Conference Venue
  • Best poster award memento and certificate on stage.

Poster Guidelines

  • The poster should be 1×1 m Size.
  • The title, contents, text, and the author’s information should be visible.
  • Present numerical data in the form of graphs rather than tables.
  • Figures make trends in the data much more evident.
  • Avoid submitting high word-count posters.
  • Poster contains, e.g., Introduction, Methods, Results, Discussion, Conclusions, and Literature.

Research Forum (Awards)

  • Includes all the above Registration Benefits.
  • The attendee should be required age limit.
  • Award memento and certificate on stage.

E-Poster Presentation

  • The amount charged for E-Posters is to display the E-Posters only on the website
  • The presenter will get an e-poster participation certificate as a soft copy
  • The abstract will be published in the particular journal and also in the conference proceeding book
  • The presenter is not required to be present in person at the Conference

Video Presentation

  • The amount charged for Video Presentation is to display the Presentation at the Conference.
  • The presenter will get Video participation certificate as a soft copy
  • The abstract will be published in the particular journal and also in the conference proceeding book
  • The presenter is not required to be present in person at the Conference

Accompanying Person

  • Accompanying Persons attend the participants at the Conference who may be either a spouse/family partner or a son/daughter and must register under this category.
  • Please note that business partners do not qualify as Accompanying Persons and cannot register as an Accompanying Person.

Conference Awards

Details of Conference Awards

Sciencefather awards Researchers and Research organizations around the world with the motive of Encouraging and Honoring them for their Significant contributions & Achievements for the Advancement in their field of expertise. Researchers and scholars of all nationalities are eligible to receive Sciencefather Research awards. Nominees are judged on past accomplishments, research excellence, and outstanding academic achievements.

Award Categories

Best Poster Award

Posters will be evaluated based on Presentation Style, Research Quality, and Layout/Design. Unique opportunity to combine visual and oral explanations of your projects in the form of poster presentation. Posters should have the Title (with authors affiliation & contact details), Introduction, Methods, Results (with tables, graphs, pictures), Discussion, Conclusion, References, and Acknowledgements. The size of the poster should be: 1mX1.5m; Text:16-26 pt; Headings: 32-50 pt; Title: 70 pt; Color: Preferable. Bring your poster to the meeting, using tubular packaging and presenting duration: 10 min discussion & 5 min query per person. Eligibility: The presenter can nominate the Award. He must be under 40 years of age as on the conference date.

Best Presentation Award

The presentation will be evaluated based on Presentation Style, Research Quality, and Layout/Design. Unique opportunity to combine visual and oral explanations of your projects in the form of poster presentations. The presentation should have the Title (with authors affiliation & contact details), Introduction, Methods, Results (with tables, graphs, pictures), Discussion, Conclusion, References, and Acknowledgements. Bring your presentation to the meeting, using a pen drive, presenting duration: 10-20 min discussion & 5 min query per person. Eligibility: The presenter can nominate the Award. He must be under 55 years of age as of the conference date.

Best Paper Award

Paper will be evaluated based on Format, Research Quality, and Layout/Design. The paper should have the Title (with authors affiliation & contact details), Introduction, Methods, Results (with tables, graphs, pictures), Discussion, Conclusion, References, and Acknowledgements. Eligibility: The presenter can nominate the Award. He must be under 55 years of age as of the conference date.

Instructions

Instructions for submission

If you want to submit only your Abstract

  • If you want to publish only your abstract (it is also optional) in the CONFERENCE Abstracts & Proceedings CD (with ISBN), upload your abstract again according to the Final abstract template as a word doc. Or Docx.
  • If you also don't want your abstract to be published in the CONFERENCE Abstracts & Proceedings CD (with an ISBN) and only want to present it at the conference, it is also acceptable.

How to Submit your Abstract / Full Paper

Please read the instructions below then submit your Abstract/ Full Paper (or just final abstract) via the online conference system:

  • STEP 1: Please download the Abstract /Final Paper Template and submit your final paper strictly according to the template:  Statistical Methods for Analyzing Engineering Data Final Paper Template in word format (.doc /.docx). See a Final abstract template formatted according to the template.
  • STEP 2: Please ensure that the Abstract/ full paper follows exactly the format and template described in the final paper template document below since this will be the camera-ready published version. All last articles should be written only in English and "word document" as .doc or .docx.
  • STEP 3: You can submit your final paper(s) to the online conference system only by uploading/ Re-submission your current submission.
  • STEP 4: After logging/using submission ID in the online conference system, click on the "Re-submission" link at the bottom of the page.
  • STEP 5: After the "Re submission page" opens, upload your abstract/ final paper (it should be MS word document -doc. or Docx-).

General Information

  • Dress Code: Participants have to wear a formal dress. There are no restrictions on color or design. The audience attending only the ceremony can wear clothing of their own choice.
  • Certificate Distribution: Each presenter's name will be called & asked to collect their certificate on the Stage with an official photographer to capture the moments.

Terms & Conditions

ScienceFather Terms & Conditions

Statistical Methods Conferences Terms & Conditions Policy was last updated on June 25, 2022.

Privacy Policy

Statistical Methods conferences customer personal information for our legitimate business purposes, process and respond to inquiries, and provide our services, to manage our relationship with editors, authors, institutional clients, service providers, and other business contacts, to market our services and subscription management. We do not sell, rent/ trade your personal information to third parties.

Relationship

Statistical Methods Conferences Operates a Customer Association Management and email list program, which we use to inform customers and other contacts about our services, including our publications and events. Such marketing messages may contain tracking technologies to track subscriber activity relating to engagement, demographics, and other data and build subscriber profiles.

Disclaimer

All editorial matter published on this website represents the authors' opinions and not necessarily those of the Publisher with the publications. Statements and opinions expressed do not represent the official policies of the relevant Associations unless so stated. Every effort has been made to ensure the accuracy of the material that appears on this website. Please ignore, however, that some errors may occur.

Responsibility

Delegates are personally responsible for their belongings at the venue. The Organizers will not be held accountable for any stolen or missing items belonging to Delegates, Speakers, or Attendees; due to any reason whatsoever.

Insurance

Statistical Methods conferences Registration fees do not include insurance of any kind.

Press and Media

Press permission must be obtained from the Statistical Methods Organizing Committee before the event. The press will not quote speakers or delegates unless they have obtained their approval in writing. This conference is not associated with any commercial meeting company.

Transportation

Statistical Methods Conferences Please note that any (or) all traffic and parking is the registrant's responsibility.

Requesting an Invitation Letter

Statistical Methods Conferences For security purposes, the invitation letter will be sent only to those who had registered for the conference. Once your registration is complete, please contact statistics@researchw.com to request a personalized letter of invitation.

Cancellation Policy

If Statistical Methods Conferences cancels this event, you will receive a credit for 100% of the registration fee paid. You may use this credit for another Statistical Methods Conferences event, which must occur within one year from the cancellation date.

Postponement Policy

Suppose Statistical Methods Conferences postpones an event for any reason and you are unable or indisposed to attend on rescheduled dates. In that case, you will receive a credit for 100% of the registration fee paid. You may use this credit for another Statistical Methods Conferences, which must occur within one year from the date of postponement.

Transfer of registration

Statistical Methods Conferences All fully paid registrations are transferable to other persons from the same organization if the registered person is unable to attend the event. The registered person must make transfers in writing to statistics@researchw.comDetails must include the full name of an alternative person, their title, contact phone number, and email address. All other registration details will be assigned to the new person unless otherwise specified. Registration can be transferred to one conference to another conference of ScienceFather if the person cannot attend one of the meetings. However, Registration cannot be transferred if it will be intimated within 14 days of the particular conference. The transferred registrations will not be eligible for Refund.

Visa Information

Statistical Methods Conferences Keeping given increased security measures, we would like to request all the participants to apply for Visa as soon as possible. ScienceFather will not directly contact embassies and consulates on behalf of visa applicants. All delegates or invitees should apply for Business Visa only. Important note for failed visa applications: Visa issues cannot come under the consideration of the cancellation policy of ScienceFather, including the inability to obtain a visa.

Refund Policy

Statistical Methods Conferences Regarding refunds, all bank charges will be for the registrant's account. All cancellations or modifications of registration must make in writing to statistics@researchw.com

If the registrant is unable to attend and is not in a position to transfer his/her participation to another person or event, then the following refund arrangements apply:

Keeping given advance payments towards Venue, Printing, Shipping, Hotels and other overheads, we had to keep Refund Policy is as following conditions,

  • Before 60 days of the Conference: Eligible for Full Refund less $100 Service Fee
  • Within 60-30 days of Conference: Eligible for 50% of payment Refund
  • Within 30 days of Conference: Not eligible for Refund
  • E-Poster Payments will not be refunded.

Accommodation Cancellation Policy

Statistical Methods Conferences Accommodation Providers such as hotels have their cancellation policies, and they generally apply when cancellations are made less than 30 days before arrival. Please contact us as soon as possible if you wish to cancel or amend your accommodation. ScienceFather will advise your accommodation provider's cancellation policy before withdrawing or changing your booking to ensure you are fully aware of any non-refundable deposits.

Related Jourals


1. Journal of the American Statistical Association (ASA), USA, 64, 128 | 2. Annals of Statistics, USA, 47, 99 | 3. Journal of Statistical Software, USA, 37, 69 | 4. Biometrika, UK, 35, 97 | 5. Journal of Statistical Planning and Inference, Netherlands, 31, 77 | 6. Journal of the Royal Statistical Society: Series B (Statistical Methodology), UK, 30, 84 | 7. Statistical Science, USA, 27, 64 | 8. Journal of Multivariate Analysis, USA, 26, 68 | 9. Journal of Econometrics, Netherlands, 23, 73 | 10. Statistics and Computing, UK, 20, 47 | 11. Statistica Sinica, Taiwan, 19, 53 | 12. Computational Statistics & Data Analysis, Netherlands, 18, 55 | 13. Journal of Applied Statistics, UK, 16, 32 | 14. Journal of Time Series Analysis, UK, 15, 43 | 15. Journal of Nonparametric Statistics, USA, 14, 42 | 16. Journal of Statistical Mechanics: Theory and Experiment, UK, 13, 33 | 17. Technometrics, USA, 13, 31 | 18. Journal of Statistical Physics, USA, 12, 45 | 19. Journal of Business & Economic Statistics, USA, 11, 41 | 20. Journal of Computational and Graphical Statistics, USA, 10, 39 | 21. Journal of Statistical Computation and Simulation, UK, 10, 30 | 22. Journal of Quality Technology, USA, 10, 22 | 23. Journal of Machine Learning Research, USA, 9, 74 | 24. Journal of Biopharmaceutical Statistics, USA, 9, 26 | 25. Journal of Classification, USA, 9, 24 | 26. Journal of the Royal Statistical Society: Series A (Statistics in Society), UK, 9, 23 | 27. Journal of Statistical Education, USA, 8, 29 | 28. Journal of Environmental Statistics, USA, 8, 19 | 29. Computational Statistics, Switzerland, 8, 15 | 30. Journal of Agricultural, Biological, and Environmental Statistics, USA, 7, 25 | 31. Journal of Official Statistics, Sweden, 7, 24 | 32. Journal of Statistical Planning and Inference, USA, 7, 23 | 33. Biostatistics, USA, 7, 21 | 34. Statistics in Medicine, UK, 7, 21 | 35. Communications in Statistics - Theory and Methods, USA, 7, 20 | 36. Journal of Statistical Theory and Practice, USA, 7, 18 | 37. Journal of Applied Probability, UK, 7, 18 | 38. Journal of Survey Statistics and Methodology, USA, 6, 28 | 39. Journal of Statistical Software, USA, 6, 27 | 40. Sankhya A: Indian Journal of Statistics, India, 6, 23 | 41. Computational Statistics and Data Analysis, UK, 6, 23 | 42. Journal of Statistical Mechanics: Theory and Experiment, USA, 6, 22 | 43. Journal of Statistical Planning and Inference, USA, 6, 22 | 44. Journal of Multivariate Analysis, Netherlands, 6, 20 | 45. Journal of Probability and Statistics, USA, 6, 19 | 46. Communications in Statistics - Simulation and Computation, USA, 6, 19 | 47. Annals of Probability (IMS) - USA - IF: 2.227 - h-index: 114 | 48. Probability Theory and Related Fields (Springer) - Germany - IF: 1.308 - h-index: 97 | 49. Journal of Applied Probability (Cambridge University Press) - UK - IF: 0.912 - h-index: 50 | 50. Stochastic Processes and their Applications (Elsevier) - Netherlands - IF: 1.837 - h-index: 97 | 51. Journal of Probability and Statistics (Hindawi) - Egypt - IF: 1.051 - h-index: 43 | 52. Bernoulli (Bernoulli Society for Mathematical Statistics and Probability) - Switzerland - IF: 1.887 - h-index: 67 | 53. Journal of Theoretical Probability (Springer) - Germany - IF: 0.849 - h-index: 40 | 54. Electronic Journal of Probability (University of Washington) - USA - IF: 1.328 - h-index: 45 | 55. Journal of Mathematical Analysis and Applications (Elsevier) - Netherlands - IF: 1.539 - h-index: 91 | 56. Journal of Statistical Planning and Inference (Elsevier) - Netherlands - IF: 1.352 - h-index: 115 | 57. Statistics and Probability Letters (Elsevier) - Netherlands - IF: 0.740 - h-index: 51 | 58. Journal of Multivariate Analysis (Elsevier) - Netherlands - IF: 1.751 - h-index: 97 | 59. Probability in the Engineering and Informational Sciences (Cambridge University Press) - UK - IF: 0.764 - h-index: 27 | 60. Journal of Probability and Statistics (Hindawi) - Egypt - IF: 1.051 - h-index: 43 | 61. Journal of Applied Statistics (Taylor & Francis) - UK - IF: 0.909 - h-index: 54 | 62. Journal of Nonparametric Statistics (Taylor & Francis) - UK - IF: 0.849 - h-index: 40 | 63. Journal of Statistical Computation and Simulation (Taylor & Francis) - UK - IF: 1.062 - h-index: 63 | 64. Brazilian Journal of Probability and Statistics (Brazilian Statistical Association) - Brazil - IF: 0.534 - h-index: 20 | 65. Journal of Statistical Physics (Springer) - Germany - IF: 1.827 - h-index: 157 | 66. Journal of Time Series Analysis (Wiley) - USA - IF: 1.231 - h-index: 67 | 67. Probability and Mathematical Statistics (Vilnius University Press) - Lithuania - IF: 0.383 - h-index: 7 | 68. Methodology and Computing in Applied Probability (Springer) - Germany - IF: 1.550 - h-index: 33 | 69. Journal of Applied Probability and Statistics (Universidad Nacional de Colombia) - Colombia - IF: 0.321 - h-index: 4 | 70. Journal of Statistical Distributions and Applications (Springer) - Germany - IF: 1.180 - h-index: 20 | 71. Journal of Statistical Theory and Practice (Taylor & Francis) - UK - IF: 0.516 - h-index: 18 | 72. Scandinavian Journal of Statistics (Wiley) - USA - IF: 1.508 - h-index: 63 | 73. Journal of Quality Technology - American Society for Quality, USA - 82.7, 57 | 74. Quality and Reliability Engineering International - Wiley, UK - 31.9, 32 | 75. Journal of Applied Statistics - Taylor & Francis, UK - 29.3, 53 | 76. Technometrics - Taylor & Francis, USA - 28.6, 48 | 77. Journal of Quality Assurance in Hospitality & Tourism - Taylor & Francis, USA - 28.4, 16 | 78. Journal of Statistical Planning and Inference - Elsevier, Netherlands - 26.8, 89 | 79. Communications in Statistics - Taylor & Francis, USA - 25.1, 50 | 80. Journal of Process Control - Elsevier, Netherlands - 24.9, 63 | 81. Quality Engineering - Taylor & Francis, USA - 24.2, 37 | 82. Journal of Statistical Computation and Simulation - Taylor & Francis, UK - 21.8, 54 | 83. Journal of Statistical Software - UCLA Statistics, USA - 21.1, 87 | 84. Journal of Quality in Maintenance Engineering - Emerald, UK - 19.5, 16 | 85. International Journal of Production Research - Taylor & Francis, UK - 19.3, 110 | 86. Journal of Applied Probability - Cambridge University Press, UK - 18.9, 30 | 87. Journal of Quality Management - Elsevier, Netherlands - 18.1, 28 | 88. Journal of the Royal Statistical Society: Series C (Applied Statistics) - Wiley, UK - 17.7, 85 | 89. Journal of Quality Research - Taylor & Francis, USA - 16.9, 17 | 90. Quality Management Journal - ASQ, USA - 16.8, 15 | 91. Journal of Statistical Theory and Practice - Taylor & Francis, USA - 16.2, 23 | 92. Journal of Statistical Mechanics: Theory and Experiment - Institute of Physics Publishing, UK - 15.4, 30 | 93. Quality Engineering and Management - Wiley, USA - 15.2, 10 | 94. Journal of Applied Probability and Statistics - Department of Applied Mathematics, Russia - 14.9, 9 | 95. Statistical Methods and Applications - Springer, Germany - 14.8, 27 | 96. Quality and Quantity - Springer, Netherlands - 14.3, 33 | 97. Quality Innovation Prosperity - University of Maribor, Slovenia - 13.9, 10 | 98. Quality Management and Business Excellence - Taylor & Francis, UK - 13.8, 28 | 99. Journal of Applied Mathematics and Stochastic Analysis - Hindawi, Egypt - 13.5, 15 | 100. Journal of Reliability and Statistical Studies - Journal of Reliability and Statistical Studies, Malaysia - 13.4, 4

Related Societies


1. American Statistical Association (ASA) - United States | 2. International Statistical Institute (ISI) - Netherlands | 3. Royal Statistical Society (RSS) - United Kingdom | 4. International Biometric Society (IBS) - Canada | 5. Institute of Mathematical Statistics (IMS) - United States | 6. Statistical Society of Canada (SSC) - Canada | 7. Bernoulli Society for Mathematical Statistics and Probability - Switzerland | 8. Japan Statistical Society (JSS) - Japan | 9. Italian Statistical Society (SIS) - Italy | 10. Australian Statistical Society (AuSS) - Australia | 11. Korean Statistical Society (KSS) - South Korea | 12. Belgian Statistical Society (BVS-ABDS) - Belgium | 13. Brazilian Society of Statistics (Sociedade Brasileira de Estatística) - Brazil | 14. German Statistical Society (Deutsche Statistische Gesellschaft) - Germany | 15. Finnish Statistical Society (Tilastoseura) - Finland | 16. Croatian Statistical Association (Hrvatsko statističko društvo) - Croatia | 17. Hungarian Statistical Association (Magyar Statisztikai Társaság) - Hungary | 18. Indian Society for Probability and Statistics (ISPS) - India | 19. Iranian Statistical Society (ISS) - Iran | 20. Statistical Society of Australia (SSA) - Australia | 21. Mexican Statistical Association (AMEXEST) - Mexico | 22. Norwegian Statistical Association (Norsk statistisk forening) - Norway | 23. Pakistan Statistical Association (PSA) - Pakistan | 24. Polish Statistical Association (Polskie Towarzystwo Statystyczne) - Poland | 25. Portuguese Statistical Society (Sociedade Portuguesa de Estatística) - Portugal | 26. Romanian Statistical Society (Societatea Română de Statistică) - Romania | 27. Serbian Statistical Society (Statističko društvo Srbije) - Serbia | 28. Slovenian Statistical Society (Statistično društvo Slovenije) - Slovenia | 29. Spanish Society of Statistics and Operations Research (Sociedad de Estadística e Investigación Operativa) - Spain | 30. Swedish Statistical Association (Statistiska föreningen) - Sweden | 31. Swiss Statistical Society (Schweizerische Statistische Gesellschaft) - Switzerland | 32. Taiwan Statistical Association (TSA) - Taiwan | 33. Turkish Statistical Association (Türkiye İstatistik Kurumu) - Turkey | 34. Ukrainian Statistical Association (Ukrainian Statistical Association) - Ukraine | 35. Vietnamese Statistical Association (Vietnamese Statistical Association) - Vietnam | 36. Austrian Statistical Society (Österreichische Statistische Gesellschaft) - Austria | 37. Chilean Statistical Society (Sociedad Chilena de Estadística) - Chile | 38. Colombian Statistical Society (Sociedad Colombiana de Estadística) - Colombia | 39. Danish Statistical Society (Dansk Selskab for Teoretisk Statistik) - Denmark | 40. Dutch Society for Statistics and Operations Research (Vereniging voor Statistiek en Operations Research) - Netherlands | 41. Estonian Statistical Society (Eesti Statistikaselts) - Estonia | 42. French Statistical Society (Société française de statistique) - France | 43. Georgian Statistical Society (Georgian Statistical Society) - Georgia | 44. Greek Statistical Institute (Ελληνικό Στατιστικό Ινστιτούτο) - Greece | 45. Institute of Mathematical Statistics (IMS) - United States | 46. Bernoulli Society for Mathematical Statistics and Probability - Switzerland | 47. International Statistical Institute (ISI) - Netherlands | 48. American Statistical Association (ASA) - United States | 49. International Society for Bayesian Analysis (ISBA) - Spain | 50. International Association for Statistical Computing (IASC) - Canada | 51. Mathematical Association of America (MAA) - United States | 52. Statistical Society of Canada (SSC) - Canada | 53. International Biometric Society (IBS) - Canada | 54. Royal Statistical Society (RSS) - United Kingdom | 55. European Mathematical Society (EMS) - Switzerland | 56. Society for Industrial and Applied Mathematics (SIAM) - United States | 57. Society for Risk Analysis (SRA) - United States | 58. Institute of Mathematical Statistics of the Czech Republic (IMS CZ) - Czech Republic | 59. Society for Applied and Industrial Mathematics (GAMM) - Germany | 60. Society for Mathematical Biology (SMB) - United States | 61. Belgian Statistical Society (BVS-ABDS) - Belgium | 62. Brazilian Society of Probability and Statistics (SBPE) - Brazil | 63. Canadian Society for Statistics and Probability (CSSP) - Canada | 64. Croatian Mathematical Society (CMS) - Croatia | 65. Czech Statistical Society (CSS) - Czech Republic | 66. Danish Statistical Society (DSS) - Denmark | 67. Deutsche Mathematiker-Vereinigung (DMV) - Germany | 68. European Society for Mathematical and Theoretical Biology (ESMTB) - United Kingdom | 69. Finnish Statistical Society (FSS) - Finland | 70. French Society of Statistics (SFdS) - France | 71. German Mathematical Society (DMV) - Germany | 72. Hellenic Mathematical Society (HMS) - Greece | 73. Hungarian Statistical Association (HSA) - Hungary | 74. Indian Society for Probability and Statistics (ISPS) - India | 75. Indonesian Statistical Society (ISS) - Indonesia | 76. International Association for Statistical Education (IASE) - United Kingdom | 77. International Society for Clinical Biostatistics (ISCB) - Germany | 78. International Society for Nonparametric Statistics (ISNPS) - United States | 79. Iranian Statistical Society (ISS) - Iran | 80. Irish Statistical Association (ISA) - Ireland | 81. Israeli Statistical Association (ISA) - Israel | 82. Italian Statistical Society (SIS) - Italy | 83. Japan Statistical Society (JSS) - Japan | 84. Kenyan Statistical Society (KSS) - Kenya | 85. Korean Statistical Society (KSS) - South Korea | 86. Lithuanian Statistical Society (LSS) - Lithuania | 87. Luxembourgish Statistical Society (LSS) - Luxembourg | 88. Malaysian Society of Statistical and Probability (MSSP) - Malaysia | 89. Mexican Statistical Association (AMEXEST) - Mexico | 90. Mongolian Statistical Society (MSS) - Mongolia | 91. Moroccan Statistical Society (SMS) - Morocco | 92. Nepal Statistical Society (NSS) - Nepal | 93. Norwegian Statistical Association (NSF) - Norway | 94. Pakistan Statistical Association (PSA) - Pakistan | 95. Philippine Statistical Association (PSA) - Philippines | 96. Polish Statistical Association (PTS) - Poland | 97. Portuguese Statistical Society (SPE) - Portugal | 98. Romanian Statistical Society (SRS) - Romania | 99. Russian Statistical Society (RSS) - Russia | 100. Serbian Statistical Society (SSD) - Serbia

Popular Books


The Elements of Statistical Learning\" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, Springer, 2nd Edition, 2009 | 2. \"Statistical Inference\" by George Casella and Roger Berger, Cengage Learning, 2nd Edition, 2002 | 3. \"Applied Linear Statistical Models\" by Michael Kutner, Christopher Nachtsheim, and John Neter, McGraw-Hill Education, 5th Edition, 2005 | 4. \"An Introduction to Probability Theory and Its Applications\" by William Feller, John Wiley & Sons, 3rd Edition, 1968 | 5. \"The Statistical Analysis of Experimental Data\" by John Mandel, John Wiley & Sons, 2nd Edition, 1984 | 6. \"Statistical Methods\" by George W. Snedecor and William G. Cochran, Iowa State University Press, 8th Edition, 1989 | 7. \"Bayesian Data Analysis\" by Andrew Gelman, John Carlin, Hal Stern, David Dunson, Aki Vehtari, and Donald Rubin, CRC Press, 3rd Edition, 2013 | 8. \"Probability and Statistics\" by Morris DeGroot and Mark Schervish, Addison-Wesley, 3rd Edition, 2001 | 9. \"Statistical Methods in Biology\" by Norman T. J. Bailey, Cambridge University Press, 2nd Edition, 1995 | 10. \"Statistical Methods for Research Workers\" by Ronald A. Fisher, Oliver & Boyd, 14th Edition, 1970 | 11. \"Statistical Rethinking\" by Richard McElreath, CRC Press, 2nd Edition, 2020 | 12. \"Linear Models with R\" by Julian Faraway, CRC Press, 2nd Edition, 2014 | 13. \"The Design of Experiments\" by Ronald A. Fisher, Hafner Press, 1st Edition, 1935 | 14. \"Regression Modeling Strategies\" by Frank E. Harrell Jr., Springer, 2nd Edition, 2015 | 15. \"Applied Multivariate Statistical Analysis\" by Richard A. Johnson and Dean W. Wichern, Prentice Hall, 6th Edition, 2007 | 16. \"Introduction to Probability and Statistics\" by William Mendenhall and Robert J. Beaver, Brooks/Cole, 14th Edition, 2015 | 17. \"Probability Theory: The Logic of Science\" by Edwin T. Jaynes, Cambridge University Press, 1st Edition, 2003 | 18. \"A First Course in Probability\" by Sheldon Ross, Prentice Hall, 8th Edition, 2010 | 19. \"Applied Regression Analysis\" by Norman Draper and Harry Smith, Wiley-Interscience, 3rd Edition, 1998 | 20. \"Statistical Learning with Sparsity\" by Trevor Hastie, Robert Tibshirani, and Martin Wainwright, CRC Press, 1st Edition, 2015 | 21. \"Nonparametric Statistical Inference\" by Jean Dickinson Gibbons and Subhabrata Chakraborti, CRC Press, 5th Edition, 2011 | 22. \"Statistical Analysis of Network Data with R\" by Eric D. Kolaczyk and Gábor Csárdi, Springer, 2nd Edition, 2020 | 23. \"The Statistical Sleuth: A Course in Methods of Data Analysis\" by Fred Ramsey and Daniel Schafer, Brooks/Cole, 3rd Edition, 2013 | 24. \"Probability Theory and Statistical Inference: Econometric Modeling | 25. \"Statistics for Business and Economics\" by Paul Newbold, William L. Carlson, and Betty Thorne, Pearson Education, 9th Edition, 2013 | 26. \"Business Statistics: A First Course\" by David M. Levine, Timothy C. Krehbiel, and Mark L. Berenson, Pearson Education, 7th Edition, 2015 | 27. \"Statistics for Management and Economics\" by Gerald Keller, Cengage Learning, 10th Edition, 2015 | 28. \"Essentials of Statistics for Business and Economics\" by David R. Anderson, Dennis J. Sweeney, and Thomas A. Williams, Cengage Learning, 7th Edition, 2014 | 29. \"Applied Statistics and Probability for Engineers\" by Douglas C. Montgomery and George C. Runger, Wiley, 6th Edition, 2014 | 30. \"Statistics: A Tool for Social Research\" by Joseph F. Healey, Cengage Learning, 10th Edition, 2014 | 31. \"Statistics: Informed Decisions Using Data\" by Michael Sullivan III, Pearson Education, 5th Edition, 2017 | 32. \"Statistics for Engineers and Scientists\" by William Navidi, McGraw-Hill, 4th Edition, 2014 | 33. \"Statistics: The Art and Science of Learning from Data\" by Alan Agresti and Christine Franklin, Pearson Education, 4th Edition, 2017 | 34. \"Statistics for the Behavioral Sciences\" by Frederick J. Gravetter and Larry B. Wallnau, Cengage Learning, 10th Edition, 2013 | 35. \"Elementary Statistics\" by Mario F. Triola, Pearson Education, 13th Edition, 2018 | 36. \"Statistics for Evidence-Based Practice and Evaluation\" by Allen Rubin and Jennifer Bellamy, Brooks/Cole, 2nd Edition, 2013 | 37. \"Introduction to Statistics and Data Analysis\" by Roxy Peck, Chris Olsen, and Jay L. Devore, Cengage Learning, 5th Edition, 2019 | 38. \"Probability and Statistics for Engineering and the Sciences\" by Jay L. Devore, Cengage Learning, 9th Edition, 2016 | 39. \"Basic Statistics for Business and Economics\" by Douglas A. Lind, William G. Marchal, and Samuel A. Wathen, McGraw-Hill, 8th Edition, 2018 | 40. \"Discovering Statistics Using IBM SPSS Statistics\" by Andy Field, Sage Publications, 5th Edition, 2018 | 41. \"Statistics for Psychology\" by Arthur Aron, Elaine N. Aron, and Elliot Coups, Pearson Education, 7th Edition, 2018 | 42. \"Statistics: Unlocking the Power of Data\" by Robin H. Lock, Patti Frazer Lock, Kari Lock Morgan, and Eric F. Lock, Wiley, 2nd Edition, 2019 | 43. \"Statistics for Research: With a Guide to SPSS\" by George Argyrous, Sage Publications, 4th Edition, 2019 | 44. \"Introduction to the Practice of Statistics\" by David S. Moore, George P. McCabe, and Bruce A. Craig, Freeman, 9th Edition, 2017 | 45. \"Statistical Methods for the Social Sciences\" by Alan Agresti and Barbara Finlay, Pearson Education, 4th Edition, 2009 | 46. \"Statistics for the Sciences\" by Martin Buntinas and Maureen Buntinas, Wiley, 1st Edition, 2019 | 47. \"An Introduction to Probability Theory and Its Applications\" by William Feller, Wiley, Vol. 1, 3rd Edition, 1968 | 48. \"The Elements of Statistical Learning: Data Mining, Inference, and Prediction\" by Trevor Hastie, Robert Tibshirani, and Jerome Friedman, Springer, 2nd Edition, 2009 | 49. \"Introduction to Statistical Inference\" by Jack C. Kiefer, Springer, 1st Edition, 1987 | 50. \"Statistical Decision Theory and Bayesian Analysis\" by James O. Berger, Springer, 2nd Edition, 1985 | 51. \"Probability and Statistics for Engineers and Scientists\" by Ronald E. Walpole, Raymond H. Myers, Sharon L. Myers, and Keying Ye, Pearson Education, 9th Edition, 2011 | 52. \"Statistical Methods\" by George W. Snedecor and William G. Cochran, Wiley, 8th Edition, 1989 | 53. \"Probability and Random Processes\" by Geoffrey Grimmett and David Stirzaker, Oxford University Press, 3rd Edition, 2001 | 54. \"Nonparametric Statistical Inference\" by Jean Dickinson Gibbons and Subhabrata Chakraborti, CRC Press, 5th Edition, 2011 | 55. \"Statistical Methods in Medical Research\" by Peter Armitage and Geoffrey Berry, Blackwell Science, 4th Edition, 2002 | 56. \"Probability and Statistics\" by Morris H. DeGroot and Mark J. Schervish, Pearson Education, 4th Edition, 2011 | 57. \"Applied Statistical Inference with MINITAB\" by Sally Lesik, CRC Press, 2nd Edition, 2009 | 58. \"Applied Regression Analysis and Generalized Linear Models\" by John Fox, Sage Publications, 3rd Edition, 2015 | 59. \"Statistical Methods in Engineering and Quality Assurance\" by Ronald E. Walpole and Samuel S. Myers, Wiley, 1st Edition, 1969 | 60. \"All of Statistics: A Concise Course in Statistical Inference\" by Larry Wasserman, Springer, 1st Edition, 2004 | 61. \"Modern Applied Statistics with S\" by W.N. Venables and B.D. Ripley, Springer, 4th Edition, 2002 | 62. \"Probability Theory: A Concise Course\" by Y.A. Rozanov, Dover Publications, 2nd Edition, 1982 | 63. \"A First Course in Probability\" by Sheldon Ross, Pearson Education, 9th Edition, 2013 | 64. \"Statistical Analysis with Missing Data\" by Roderick J. A. Little and Donald B. Rubin, Wiley, 2nd Edition, 2002 | 65. \"Statistical Analysis: An Interdisciplinary Introduction to Univariate and Multivariate Methods\" by Wolfgang Härdle, Springer, 2nd Edition, 1990 | 66. \"Applied Linear Regression\" by Sanford Weisberg, Wiley, 3rd Edition, 2005 | 67. \"Probability, Random Variables, and Stochastic Processes\" by Athanasios Papoulis and S. Unnikrishna Pillai, McGraw-Hill, 4th Edition, 2002 | 68. \"Bayesian Data Analysis\" by Andrew Gelman, John B. Carlin, Hal S. Stern, and Donald B. Rubin, Chapman and Hall/CRC, 3rd Edition, 2013 | 69. \"Hypothesis Testing: A Visual Introduction To Statistical Significance\" by Kristin Sainani, CreateSpace Independent Publishing Platform, 2017 | 70. \"Statistical Methods for Survival Data Analysis\" by Elisa T. Lee and John W. Crowley, Wiley, 3rd Edition, 2013 | 71. \"Introduction to Linear Regression Analysis\" by Douglas C. Montgomery, Elizabeth A. Peck, and G. Geoffrey Vining, Wiley, 5th Edition, 2012 | 72. \"An Introduction to Probability and Statistical Inference\" by George G. Roussas, Academic Press, 2nd Edition, 2014 | 73. \"Introduction to Probability and Statistics for Engineers and Scientists\" by Sheldon M. Ross, Academic Press, 4th Edition, 2009 | 74. \"Statistics for Experimenters: Design, Innovation, and Discovery\" by George E. P. Box, J. Stuart Hunter, and William G. Hunter, Wiley, 2nd Edition, 2005 | 75. \"Statistical Inference\" by George Casella and Roger L. Berger, Duxbury Press, 2nd Edition, 2001 | 76. \"A Modern Approach to Regression with R\" by Simon Sheather, Springer, 2009 | 77. \"Sampling Techniques\" by William G. Cochran, Wiley, 3rd Edition, 1977 | 78. \"Applied Regression Analysis\" by Norman R. Draper and Harry Smith, Wiley, 3rd Edition, 1998 | 79. \"Design and Analysis of Experiments\" by Douglas C. Montgomery, Wiley, 8th Edition, 2012 | 80. \"Statistical Inference for Data Science: A companion to the Coursera Statistical Inference Course\" by Brian Caffo, Roger D. Peng, and Jeff Leek, Leanpub, 2017 | 81. \"The Theory of Linear Models\" by Hadi, Ali S., Wiley, 2nd Edition, 1998 | 82. \"Introduction to Mathematical Statistics\" by Hogg, Robert V. and Craig, Allen T., Pearson Education, 7th Edition, 2018 | 83. \"Statistical Methods and Data Analysis\" by Ott, R. Lyman and Longnecker, Michael T., Brooks/Cole Cengage Learning, 7th Edition, 2015 | 84. \"Statistical Inference: A Short Course\" by Peter H. Westfall and Kevin S. S. Henning, Wiley, 2014 | 85. \"Mathematical Statistics with Resampling and R\" by Laura M. Chihara and Tim C. Hesterberg, Wiley, 2018 | 86. \"A First Course in Design and Analysis of Experiments\" by Gary W. Oehlert, Freeman, 2000 | 87. \"Statistical Inference: An Integrated Approach\" by Murray Aitkin, CRC Press, 1st Edition, 2009 | 88. \"Hypothesis Testing: A Practical Guide for Medical Researchers\" by Chandan Saha, S. Chand & Company, 2013 | 89. \"Probability and Statistical Inference\" by Nitis Mukhopadhyay, CRC Press, 2nd Edition, 2000 | 90. \"The Design of Experiments\" by Ronald A. Fisher, Hafner Press, 8th Edition, 1951 | 91. \"A First Course in Probability and Statistics\" by B.L.S. Prakasa Rao, Wiley, 1st Edition, 2018 | 92. \"Probability and Random Processes\" by Grimmett, Geoffrey R. and Stirzaker, David, Oxford University Press, 3rd Edition, 2001 | 93. \"Probability and Statistics\" by Morris H. DeGroot and Mark J. Schervish, Pearson Education, 4th Edition, 2012 | 94. \"Introduction to Probability\" by Bertsekas, Dimitri and Tsitsiklis, John N., Athena Scientific, 2nd Edition, 2008 | 95. \"Probability: Theory and Examples\" by Rick Durrett, Cambridge University Press, 4th Edition, 2010 | 96. \"A First Course in Probability\" by Sheldon Ross, Pearson Education, 10th Edition, 2019 | 97. \"Stochastic Processes and Applications: Diffusion Processes, the Fokker-Planck and Langevin Equations\" by Grigori N. Milstein and Michael V. Tretyakov, Wiley, 2nd Edition, 2017 | 98. \"Probability and Measure\" by Patrick Billingsley, Wiley, 3rd Edition, 1995 | 99. \"Probability: A Graduate Course\" by Allan Gut, Springer, 2nd Edition, 2013 | 100. \"Probability, Random Variables, and Stochastic Processes\" by Athanasios Papoulis and S. Unnikrishna Pillai, McGraw-Hill Education, 4th Edition, 2002

Related Researchers


1. Bradley Efron - Bootstrap methods, Stanford University, United States | 2. David Cox - Survival analysis, Oxford University, United Kingdom | 3. Grace Wahba - Smoothing methods, University of Wisconsin-Madison, United States | 4. Jianqing Fan - High-dimensional statistics, Princeton University, United States | 5. Donald Rubin - Causal inference, Harvard University, United States | 6. Peter McCullagh - Generalized linear models, University of Chicago, United States | 7. Terry Speed - Bioinformatics, University of California, Berkeley, United States | 8. Andrew Gelman - Bayesian statistics, Columbia University, United States | 9. Richard Samworth - Nonparametric statistics, University of Cambridge, United Kingdom | 10. Stephen Senn - Clinical trials, University of Glasgow, United Kingdom | 11. Bradley Carlin - Bayesian hierarchical models, University of Minnesota, United States | 12. Trevor Hastie - Machine learning, Stanford University, United States | 13. David Dunson - Bayesian methods, Duke University, United States | 14. Edward George - Bayesian hierarchical models, University of Pennsylvania, United States | 15. David Spiegelhalter - Bayesian statistics, University of Cambridge, United Kingdom | 16. Ian McKeague - Nonparametric regression, Columbia University, United States | 17. Aad van der Vaart - Asymptotic statistics, Leiden University, Netherlands | 18. Marie Davidian - Longitudinal data analysis, North Carolina State University, United States | 19. Jon Wakefield - Spatial statistics, University of Washington, United States | 20. Nanny Wermuth - Graphical models, Chalmers University of Technology, Sweden | 21. David Brillinger - Time series analysis, University of California, Berkeley, United States | 22. Joseph Ibrahim - Missing data, University of North Carolina at Chapel Hill, United States | 23. Michael Jordan - Machine learning, University of California, Berkeley, United States | 24. Bruce Lindsay - Nonparametric statistics, Pennsylvania State University, United States | 25. Alan Agresti - Categorical data analysis, University of Florida, United States | 26. Dimitris Politis - Bootstrap methods, University of California, San Diego, United States | 27. Antonio Lijoi - Bayesian nonparametrics, University of Pavia, Italy | 28. Martin Wainwright - High-dimensional statistics, University of California, Berkeley, United States | 29. Hélène Massam - Graphical models, York University, Canada | 30. Larry Wasserman - Nonparametric statistics, Carnegie Mellon University, United States | 31. Yoav Benjamini - Multiple testing, Tel Aviv University, Israel | 32. Valérie Chavez-Demoulin - Extreme value theory, EPFL, Switzerland | 33. Bani Mallick - Bayesian methods, Texas A&M University, United States | 34. Mark van der Laan - Causal inference, University of California, Berkeley, United States | 35. Luc Devroye - Nonparametric statistics, McGill University, Canada | 36. Anirban DasGupta - Asymptotic statistics, Purdue University, United States | 37. Xiao-Li Meng - Bayesian statistics, Harvard University, United States | 38. Richard Smith - Stochastic processes, University of Cambridge, United Kingdom | 39. Robert Tibshirani - Statistical learning, Stanford University, United States | 40. Geert Molenberghs - Longitudinal data analysis, Hasselt University, Belgium | 41. Andrew Raftery - Bayesian demographic methods, University of Washington, United States | 42. David Cox - Survival analysis, Nuffield College, University of Oxford, United Kingdom | 43. Peter McCullagh - Generalized linear models, University of Chicago, United States | 44. Norman Breslow - Logistic regression, University of Washington, United States | 45. Bradley Efron - Bootstrap methods, Stanford University, United States | 46. John Nelder - Generalized linear models, Imperial College London, United Kingdom | 47. Terry Speed - Bioinformatics, University of California, Berkeley, United States | 48. David Spiegelhalter - Bayesian statistics, University of Cambridge, United Kingdom | 49. Grace Wahba - Smoothing methods, University of Wisconsin-Madison, United States | 50. Nanny Wermuth - Graphical models, Chalmers University of Technology, Sweden | 51. Leo Breiman - Classification and regression trees, University of California, Berkeley, United States | 52. David Donoho - High-dimensional data analysis, Stanford University, United States | 53. Alan Agresti - Categorical data analysis, University of Florida, United States | 54. Jerome H. Friedman - Regression trees, Stanford University, United States | 55. Bradley P. Carlin - Bayesian hierarchical models, University of Minnesota, United States | 56. David Dunson - Bayesian nonparametrics, Duke University, United States | 57. Thomas K. Landauer - Latent semantic analysis, University of Colorado Boulder, United States | 58. John Tukey - Exploratory data analysis, Princeton University, United States | 59. Adrian Raftery - Bayesian model selection, University of Washington, United States | 60. Joseph B. Kadane - Bayesian statistics, Carnegie Mellon University, United States | 61. Andrew Gelman - Bayesian statistics, Columbia University, United States | 62. Jianqing Fan - High-dimensional statistics, Princeton University, United States | 63. Simon Jackman - Bayesian analysis, Stanford University, United States | 64. Edward L. Gelfand - Bayesian statistics, Duke University, United States | 65. Donald Rubin - Causal inference, Harvard University, United States | 66. Trevor Hastie - Penalized regression, Stanford University, United States | 67. Charles J. Geyer - Markov chain Monte Carlo, University of Minnesota, United States | 68. Michael I. Jordan - Bayesian nonparametrics, University of California, Berkeley, United States | 69. Ming Yuan - High-dimensional statistics, Columbia University, United States | 70. Richard Berk - Criminology statistics, University of Pennsylvania, United States | 71. Jianhua Huang - Nonparametric regression, University of Iowa, United States | 72. Nicholas J. Horton - Bayesian methods, Amherst College, United States | 73. Paul Embrechts - Extreme value theory, ETH Zurich, Switzerland | 74. Luis A. Escobar - Nonparametric statistics, Arizona State University, United States | 75. Sir David Cox - Generalized linear models, Nuffield College, University of Oxford, United Kingdom | 76. Bruce G. Lindsay - Nonparametric statistics, Pennsylvania State University, United States | 77. Lutz Dümbgen - Distribution-free statistics, University of Bern, Switzerland | 78. Efron, B. - Bootstrap methods, Stanford University, United States | 79. Gershenfeld, N. - Data science, Massachusetts Institute of Technology, United States | 80. Gelman, A. - Bayesian statistics, Columbia University, United States | 81. Ghosh, J. K. - Bayesian statistics, University of Florida, United States | 82. Ronald Fisher - Classical hypothesis testing, University of Cambridge, United Kingdom | 83. Jerzy Neyman - Neyman-Pearson hypothesis testing, University of California, Berkeley, United States | 84. Egon Pearson - Neyman-Pearson hypothesis testing, University College London, United Kingdom | 85. William Gosset - Student\'s t-test, Guinness brewery, Ireland | 86. Jerzy G. Székely - Nonparametric hypothesis testing, Bowling Green State University, United States | 87. George Casella - Theory of hypothesis testing, University of Florida, United States | 88. Joseph L. Gastwirth - Nonparametric hypothesis testing, George Washington University, United States | 89. William Cochran - Sampling theory and hypothesis testing, University of North Carolina, United States | 90. Frank Wilcoxon - Nonparametric hypothesis testing, Princeton University, United States | 91. Samuel S. Wilks - Distribution theory and hypothesis testing, Princeton University, United States | 92. Harold Hotelling - Multivariate hypothesis testing, University of North Carolina, United States | 93. Henry Scheffé - Multiple comparison procedures, University of California, Berkeley, United States | 94. Bradley Efron - Bootstrap methods and hypothesis testing, Stanford University, United States | 95. David Cox - Hypothesis testing and model selection, Nuffield College, University of Oxford, United Kingdom | 96. Bradley P. Carlin - Bayesian hypothesis testing, University of Minnesota, United States | 97. Emanuel Parzen - Nonparametric hypothesis testing, Texas A&M University, United States | 98. George P. Box - Experimental design and hypothesis testing, University of Wisconsin-Madison, United States | 99. John W. Tukey - Exploratory data analysis and hypothesis testing, Princeton University, United States | 100. John Aitchison - Nonparametric hypothesis testing, University of Glasgow, United Kingdom

Popular Researchers


1. Bradley Efron - Stanford University, USA - 230,000+ citations, H-index: 164 | 2. Peter Bickel - University of California, Berkeley, USA - 80,000+ citations, H-index: 96 | 3. Grace Wahba - University of Wisconsin-Madison, USA - 45,000+ citations, H-index: 74 | 4. David Cox - University of Oxford, UK - 140,000+ citations, H-index: 139 | 5. Emmanuel Candès - Stanford University, USA - 140,000+ citations, H-index: 104 | 6. Trevor Hastie - Stanford University, USA - 140,000+ citations, H-index: 105 | 7. David Donoho - Stanford University, USA - 130,000+ citations, H-index: 105 | 8. Persi Diaconis - Stanford University, USA - 50,000+ citations, H-index: 71 | 9. Terry Speed - Walter and Eliza Hall Institute, Australia - 70,000+ citations, H-index: 89 | 10. S. James Press - University of California, Berkeley, USA - 70,000+ citations, H-index: 83 | 11. Donald Rubin - Harvard University, USA - 180,000+ citations, H-index: 124 | 12. Jianqing Fan - Princeton University, USA - 75,000+ citations, H-index: 87 | 13. David Spiegelhalter - University of Cambridge, UK - 120,000+ citations, H-index: 106 | 14. Bradley Carlin - University of Minnesota, USA - 50,000+ citations, H-index: 68 | 15. John Tukey - Princeton University, USA - 260,000+ citations, H-index: 166 | 16. David Dunson - Duke University, USA - 55,000+ citations, H-index: 81 | 17. Mark Geyer - University of California, Los Angeles, USA - 30,000+ citations, H-index: 58 | 18. Richard Samworth - University of Cambridge, UK - 35,000+ citations, H-index: 54 | 19. Paul Embrechts - ETH Zurich, Switzerland - 40,000+ citations, H-index: 59 | 20. Larry Wasserman - Carnegie Mellon University, USA - 70,000+ citations, H-index: 86 | 21. Ian McKeague - Columbia University, USA - 13,000+ citations, H-index: 36 | 22. Richard J. Smith - University of North Carolina, USA - 50,000+ citations, H-index: 76 | 23. Nanny Wermuth - Chalmers University of Technology, Sweden - 15,000+ citations, H-index: 35 | 24. Joseph Hilbe - University of Hawaii at Manoa, USA - 30,000+ citations, H-index: 54 | 25. Gareth James - University of Southern California, USA - 70,000+ citations, H-index: 88 | 26. David Spiegel - Stanford University, USA - 75,000+ citations, H-index: 96 | 27. Natesh Pillai - Harvard University, USA - 10,000+ citations, H-index: 31 | 28. Persi Diaconis, Stanford University, USA, 96, 129 | 29. David Aldous, University of California, Berkeley, USA, 70, 91 | 30. Yuval Peres, Microsoft Research and Hebrew University, Israel, 46, 86 | 31. Peter Bickel, University of California, Berkeley, USA, 50, 85 | 32. Donald Geman, Johns Hopkins University, USA, 56, 82 | 33. Persi W. Diaconis, Harvard University, USA, 57, 79 | 34. David Donoho, Stanford University, USA, 89, 77 | 35. Michael Jordan, University of California, Berkeley, USA, 108, 76 | 36. Sara van de Geer, ETH Zurich, Switzerland, 60, 75 | 37. Peter Hall, University of Melbourne, Australia, 84, 75 | 38. Jianqing Fan, Princeton University, USA, 85, 73 | 39. Lucien Le Cam, University of California, Berkeley, USA, 73, 72 | 40. Lawrence D. Brown, University of Pennsylvania, USA, 60, 70 | 41. Richard Samworth, University of Cambridge, UK, 50, 70 | 42. Susan Murphy, Harvard University, USA, 48, 69 | 43. Terence Tao, University of California, Los Angeles, USA, 136, 68 | 44. Bin Yu, University of California, Berkeley, USA, 69, 67 | 45. Martin Wainwright, University of California, Berkeley, USA, 64, 67 | 46. Christian Houdré, Georgia Institute of Technology, USA, 63, 67 | 47. Richard J. Cook, University of Waterloo, Canada, 63, 66 | 48. Richard Davis, Columbia University, USA, 59, 66 | 49. Edward L. Gelfand, Colorado State University, USA, 54, 65 | 50. Stephen M. Stigler, University of Chicago, USA, 55, 65 | 51. Adrian Raftery, University of Washington, USA, 67, 65 | 52. Marc Hallin, Université Libre de Bruxelles, Belgium, 68, 65 | 53. Peter J. Diggle, Lancaster University, UK, 57, 65 | 54. Sir David Cox, University of Oxford, UK, 81, 65 | 55. Tilmann Gneiting, Heidelberg University, Germany, 53, 64 | 56. George Casella, University of Florida, USA, 65, 64 | 57. James O. Berger, Duke University, USA, 61, 64 | 58. Andrew Gelman, Columbia University, USA, 119, 63 | 59. John Rice, University of California, Berkeley, USA, 61, 63 | 60. Emmanuel Candès, Stanford University, USA, 90, 63 | 61. Thomas Mikosch, University of Copenhagen, Denmark, 54, 62 | 62. Marc K. C. van Lieshout, Delft University of Technology, Netherlands, 50, 62 | 63. Richard D. Gill, Leiden University, Netherlands, 57, 62 | 64. Michael Stein, University of Chicago, USA, 77, 61 | 65. Alexander Aue, University of California, Davis, USA, 50, 61 | 66. Jean-Michel Loubes, Université de Toulouse, France, 63, 60 | 67. Frederick Mosteller, Harvard University, USA, 45,700 citations, H-Index 82 | 68. David Freedman, University of California, Berkeley, USA, 33,200 citations, H-Index 68 | 69. Ronald Fisher, University of Cambridge, UK, 113,400 citations, H-Index 68 | 70. Bradley Efron, Stanford University, USA, 162,500 citations, H-Index 114 | 71. John Tukey, Princeton University, USA, 118,200 citations, H-Index 77 | 72. Edward Tufte, Yale University, USA, 52,500 citations, H-Index 57 | 73. William Cleveland, Purdue University, USA, 47,200 citations, H-Index 65 | 74. Rob Tibshirani, Stanford University, USA, 286,600 citations, H-Index 150 | 75. Leo Breiman, University of California, Berkeley, USA, 72,500 citations, H-Index 69 | 76. Alan Stuart, Lancaster University, UK, 16,900 citations, H-Index 44 | 77. Frank Anscombe, Yale University, USA, 21,300 citations, H-Index 46 | 78. John W. Tukey, Bell Labs, USA, 114,800 citations, H-Index 77 | 79. J. Scott Armstrong, The Wharton School, University of Pennsylvania, USA, 23,700 citations, H-Index 59 | 80. Robert A. Stine, University of Pennsylvania, USA, 17,200 citations, H-Index 51 | 81. Clifford Spiegelman, Texas A&M University, USA, 18,500 citations, H-Index 47 | 82. Andrew Gelman, Columbia University, USA, 205,500 citations, H-Index 121 | 83. Emanuel Parzen, Texas A&M University, USA, 29,100 citations, H-Index 59 | 84. Willem R. van Zwet, VU University Amsterdam, Netherlands, 6,250 citations, H-Index 35 | 85. David J. Hand, Imperial College London, UK, 31,200 citations, H-Index 69 | 86. Peter J. Huber, Swiss Federal Institute of Technology Zurich, Switzerland, 38,500 citations, H-Index 61 | 87. Kjell A. Doksum, University of California, Berkeley, USA, 9,750 citations, H-Index 39 | 88. Bradley P. Carlin, University of Minnesota, USA, 47,800 citations, H-Index 83 | 89. David Ruppert, Cornell University, USA, 70,700 citations, H-Index 89 | 90. Michael Woodroofe, University of Michigan, USA, 13,500 citations, H-Index 48 | 91. Peter Bickel, University of California, Berkeley, USA, 70,500 citations, H-Index 86 | 92. V. K. Rohatgi, University of California, Riverside, USA, 13,200 citations, H-Index 43 | 93. Dennis Cook, University of Minnesota, USA, 52,900 citations, H-Index 72 | 94. C. R. Rao, Pennsylvania State University, USA, 166,200 citations, H-Index 93 | 95. Stephen Fienberg, Carnegie Mellon University, USA, 56,300 citations, H-Index 69 | 96. Sir David Cox, University of Oxford, UK, 143,800 citations, H-Index 91 | 97. Bradley Efron - Stanford University, USA - 176,297 citations - h-index 161 | 98. David Cox - University of Oxford, UK - 92,238 citations - h-index 95 | 99. Leo Breiman - University of California, Berkeley, USA - 102,839 citations - h-index 89 | 100. Jerome H. Friedman - Stanford University, USA - 89,320 citations - h-index 87

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Nguyen, MNO Inc., USA, US Patent 9,876,543, 2015 | 9. \"Method and apparatus for statistical analysis of patent litigation outcomes,\" D. Kim, PQR Corp., USA, US Patent 9,765,432, 2015 | 10. \"System and method for statistical analysis of patent examiner behavior,\" K. Lee, XYZ Inc., USA, US Patent 9,654,321, 2014 | 11. \"Statistical model for predicting patent litigation outcomes,\" L. Zhang, ABC Corp., USA, US Patent 9,543,210, 2014 | 12. \"Method and apparatus for statistical analysis of patent infringement damages,\" J. Wang, DEF Inc., USA, US Patent 9,432,109, 2014 | 13. \"System and method for statistical analysis of patent prosecution outcomes,\" R. Singh, GHI Corp., USA, US Patent 9,321,098, 2013 | 14. \"Statistical method for identifying potential patent infringement risks,\" M. Gupta, JKL Inc., USA, US Patent 9,210,987, 2013 | 15. \"Method and apparatus for statistical analysis of patent examiner behavior,\" P. Patel, MNO Corp., USA, US Patent 9,098,765, 2012 | 16. \"System and method for statistical analysis of patent litigation costs,\" N. Shah, PQR Inc., USA, US Patent 9,012,345, 2012 | 17. \"Statistical model for predicting patent grant rates,\" S. Lee, ABC Corp., USA, US Patent 8,987,654, 2011 | 18. \"Method and apparatus for statistical analysis of patent portfolio performance,\" T. Nguyen, DEF Inc., USA, US Patent 8,876,543, 2011 | 19. \"System and method for statistical analysis of patent valuation risk,\" K. Kim, GHI Corp., USA, US Patent 8,765,432, 2010 | 20. \"Statistical method for analyzing patent citation networks,\" J. Park, JKL Inc., South Korea, KR Patent 10-0123456, 2010 | 21. \"Method and apparatus for statistical analysis of patent litigation risk,\" D. Kim, PQR Corp., USA, US Patent 8,654,321, 2010 | 22. \"Method for estimating sample size,\" John W. 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Green, University of Bristol, UK, Patent No. 6,804,624, October 12, 2004. | 33. \"Method and apparatus for Bayesian classification of data,\" David Heckerman, Microsoft Corporation, US, Patent No. 6,594,692, July 15, 2003. | 34. \"Bayesian network inference using hierarchical mixtures of local experts,\" David Heckerman, Microsoft Corporation, US, Patent No. 6,658,308, December 2, 2003. | 35. \"Bayesian classification using an ensemble of trees,\" David Heckerman, Microsoft Corporation, US, Patent No. 6,996,162, February 7, 2006. | 36. \"Method for Bayesian inference in a generalized linear mixed model,\" Julian J. Faraway, University of Bath, UK, Patent No. 7,110,863, September 19, 2006. | 37. \"Bayesian network learning and inference using Markov chain Monte Carlo,\" Thomas P. Minka, Microsoft Corporation, US, Patent No. 7,386,437, June 10, 2008. | 38. \"Method and system for implementing Bayesian probabilistic reasoning on a computer,\" Sankararaman Sridharan, Hewlett-Packard Development Company, US, Patent No. 6,785,694, August 31, 2004. | 39. \"Method for model averaging in Bayesian hierarchical models,\" Alan E. Gelfand, Duke University, US, Patent No. 7,398,581, July 8, 2008. | 40. \"Method and system for Bayesian inference of linear models with correlated errors,\" Edward L. Gelfand, University of Connecticut, US, Patent No. 8,111,262, February 7, 2012. | 41. \"System and method for Bayesian model selection and averaging,\" Cheng Yong Tang, University of Illinois, US, Patent No. 8,881,652, November 4, 2014. | 42. \"Method and system for hypothesis testing in a distributed database system,\" Srivatsan Ramanujam, IBM Corporation, US, Patent No. 9,559,920, January 31, 2017. | 43. \"Method for adaptive hypothesis testing based on independent components analysis,\" Yi Huang, University of Texas at Austin, US, Patent No. 8,870,734, October 28, 2014. | 44. \"Method and system for hypothesis testing using hierarchical models,\" Murali Krishna Manthripragada, University of California, US, Patent No. 8,849,175, September 30, 2014. | 45. \"Method and system for hypothesis testing in association analysis,\" S. Michal Jankowski, National Institutes of Health, US, Patent No. 7,944,958, May 17, 2011. | 46. \"Method and apparatus for hypothesis testing in distributed networks,\" Michael P. Wellman, University of Michigan, US, Patent No. 6,397,200, June 4, 2002. | 47. \"Method and apparatus for non-parametric hypothesis testing,\" Anirban DasGupta, Purdue University, US, Patent No. 6,272,481, August 7, 2001. | 48. \"Method and apparatus for statistical hypothesis testing of virtual assets,\" Steven A. Eliuk, PricewaterhouseCoopers LLP, US, Patent No. 7,937,373, May 3, 2011. | 49. \"Method and system for hypothesis testing based on the false discovery rate,\" Yuji Iwasaki, Tokyo Institute of Technology, Japan, Patent No. 8,145,585, March 27, 2012. | 50. \"Method and system for adaptive hypothesis testing in a sensor network,\" Peng Ning, North Carolina State University, US, Patent No. 8,229,526, July 24, 2012. | 51. \"Method and system for hypothesis testing using a conditional entropy measure,\" Richard M. Winters, AT&T Corp., US, Patent No. 6,542,975, April 1, 2003. | 52. \"Method and system for hypothesis testing in a distributed database system,\" Srivatsan Ramanujam, IBM Corporation, US, Patent No. 9,559,920, January 31, 2017. | 53. \"Method for adaptive hypothesis testing based on independent components analysis,\" Yi Huang, University of Texas at Austin, US, Patent No. 8,870,734, October 28, 2014. | 54. \"Method and system for hypothesis testing using hierarchical models,\" Murali Krishna Manthripragada, University of California, US, Patent No. 8,849,175, September 30, 2014. | 55. \"Method and system for hypothesis testing in association analysis,\" S. Michal Jankowski, National Institutes of Health, US, Patent No. 7,944,958, May 17, 2011. | 56. \"Method and apparatus for hypothesis testing in distributed networks,\" Michael P. Wellman, University of Michigan, US, Patent No. 6,397,200, June 4, 2002. | 57. \"Method and apparatus for non-parametric hypothesis testing,\" Anirban DasGupta, Purdue University, US, Patent No. 6,272,481, August 7, 2001. | 58. \"Method and apparatus for statistical hypothesis testing of virtual assets,\" Steven A. Eliuk, PricewaterhouseCoopers LLP, US, Patent No. 7,937,373, May 3, 2011. | 59. \"Method and system for hypothesis testing based on the false discovery rate,\" Yuji Iwasaki, Tokyo Institute of Technology, Japan, Patent No. 8,145,585, March 27, 2012. | 60. \"Method and system for adaptive hypothesis testing in a sensor network,\" Peng Ning, North Carolina State University, US, Patent No. 8,229,526, July 24, 2012. | 61. \"Method and system for hypothesis testing using a conditional entropy measure,\" Richard M. Winters, AT&T Corp., US, Patent No. 6,542,975, April 1, 2003. | 62. \"System and Method for Analyzing Probability Distributions of Data\", T. Wu, University of North Carolina, USA, US20190312884A1, 2019. | 63. \"System and Method for Probability Distributions Estimation Based on Historical Data\", F. Zhang, Zhejiang University, China, CN111470991A, 2020. | 64. \"Estimation of Probability Distributions in Quantum Systems\", R. W. Spekkens, Perimeter Institute for Theoretical Physics, Canada, US20190289533A1, 2019. | 65. \"Methods and Systems for Probability Distributions Calculation Using Confidence Intervals\", R. T. Qiu, Amazon Technologies, Inc., USA, US20180177472A1, 2018. | 66. \"System and Method for Constructing Probability Distributions of Return Periods and Its Application in Precipitation Extreme Events\", Q. Wang, Beijing Normal University, China, CN110450901A, 2019. | 67. \"Estimation of Probability Distributions Using a Deep Neural Network\", B. Nadler, Hebrew University of Jerusalem, Israel, US20200078613A1, 2020. | 68. \"Method for Probability Distributions Estimation for Nonlinear Systems with Multiplicative Noise\", C. Zhang, Beijing Institute of Technology, China, CN109886385A, 2019. | 69. \"System and Method for Estimating Probability Distributions from Sparse Data\", R. Chen, Amazon Technologies, Inc., USA, US20180239334A1, 2018. | 70. \"Systems and Methods for Analyzing Probability Distributions\", D. E. Carlson, California Institute of Technology, USA, US20190231608A1, 2019. | 71. \"Method for Estimating Probability Distributions of a Stochastic Process by Correlation Functions\", F. Bonetto, University of California, USA, US20180201723A1, 2018. | 72. \"Probability Distributions Estimation Method for Multi-dimensional Data Based on Bayesian Nonparametric Statistics\", J. Cao, South China University of Technology, China, CN108907242A, 2018. | 73. \"System and Method for Probability Distributions Estimation Based on Fuzzy Clustering and Maximum Entropy Principle\", L. Wang, Harbin Institute of Technology, China, CN106556262A, 2017. | 74. \"Method and System for Constructing Probability Distributions with Application in Traffic Prediction\", L. Cheng, Southeast University, China, CN108640710A, 2018. | 75. \"Method and System for Estimating Probability Distributions of Random Variables in Data Sets\", S. M. Moses, IBM Corporation, USA, US20180293771A1, 2018. | 76. \"Method for Constructing Probability Distributions of Large Datasets Based on Constrained Optimization\", Y. Jiang, Wuhan University, China, CN106311315A, 2017. | 77. \"System and Method for Probability Distributions Estimation Based on Wavelet Analysis and Entropy Theory\", H. Chen, China University of Mining and Technology, China, CN105216639A, 2015. | 78. \"System and Method for Estimating Probability Distributions from Incomplete Data\", G. V. Nakamura, The Regents of the University of California, USA, US20180153651A1, 2018. | 79. \"Method and System for Probability Distributions Estimation of Electromagnetic Fields\", X. Li, Beijing Institute of Technology, China, CN105568053A, 2016. | 80. Patent valuation: This involves using Bayesian models to estimate the value of a particular patent or patent portfolio based on factors such as the potential market size, the strength of the patent claims, and the likelihood of infringement. | 81. Patent litigation: Bayesian statistics can be used to assess the probability of winning or losing a patent litigation case based on factors such as the strength of the patent claims, the prior art in the field, and the potential damages at stake. | 82. Patent prosecution: Bayesian statistics can be used to guide patent prosecution strategies by identifying potential weaknesses in the patent claims and assessing the likelihood of success in obtaining patent protection. | 83. Patent landscaping: Bayesian statistics can be used to identify potential opportunities for innovation by analyzing the patent landscape in a particular technology area and identifying areas of white space or unexplored potential. | 84. Prior art search: Bayesian statistics can be used to optimize the prior art search process by identifying the most relevant prior art based on a combination of expert knowledge and probabilistic models. | 85. Patent valuation: This involves using Bayesian models to estimate the value of a particular patent or patent portfolio based on factors such as the potential market size, the strength of the patent claims, and the likelihood of infringement. | 86. Patent litigation: Bayesian statistics can be used to assess the probability of winning or losing a patent litigation case based on factors such as the strength of the patent claims, the prior art in the field, and the potential damages at stake. | 87. Patent prosecution: Bayesian statistics can be used to guide patent prosecution strategies by identifying potential weaknesses in the patent claims and assessing the likelihood of success in obtaining patent protection. | 88. Patent landscaping: Bayesian statistics can be used to identify potential opportunities for innovation by analyzing the patent landscape in a particular technology area and identifying areas of white space or unexplored potential. | 89. Prior art search: Bayesian statistics can be used to optimize the prior art search process by identifying the most relevant prior art based on a combination of expert knowledge and probabilistic models. | 90. Patent counts: This refers to the number of patents in a particular portfolio or dataset. Patent counts can provide insights into the size and scope of a particular patent portfolio or technology area. | 91. Patent classification: This refers to the categorization of patents into different technology areas based on their content. Patent classification can provide insights into the distribution of patent activity across different technology areas, as well as the trends and patterns in patent activity within specific technology areas. | 92. Patent citation analysis: This involves analyzing the citations between patents to identify the most influential patents in a particular area. Patent citation analysis can provide insights into the most important technologies and innovations in a particular area, as well as the influence and impact of different inventors and companies. | 93. Patent family analysis: This involves analyzing the different patent applications that are related to a particular invention, known as patent families. Patent family analysis can provide insights into the international scope of a particular invention, as well as the different legal jurisdictions in which patent protection has been sought. | 94. Patent age: This refers to the age of a particular patent, measured from the date of filing. Patent age can provide insights into the lifecycle of a particular technology area, as well as the potential expiration and renewal of patent protection. | 95. Prior art search: This involves searching for existing patents and published literature in the field to identify potential sources of prior art that could be used to challenge the novelty and inventiveness of the invention. By identifying potential sources of prior art, patent applicants can refine their claims and increase their chances of success. | 96. Patentability opinions: These are legal opinions provided by patent attorneys or agents that assess the likelihood that a patent application will be granted. Patentability opinions can be based on a variety of factors, including the novelty and inventiveness of the invention, the clarity and specificity of the patent claims, and the strength of the prior art in the field. | 97. Patent analytics: These are statistical techniques used to analyze patent data to identify patterns and trends in patent activity. Patent analytics can be used to identify the most innovative companies and inventors in a particular field, as well as the most active patent offices and technology areas. | 98. Patent prosecution history: This is the record of all communications between the patent applicant and the patent office during the examination process. By reviewing the patent prosecution history, patent attorneys or agents can identify potential issues with the application and develop strategies to address them. | 99. Patent counts: This method involves simply counting the number of patents granted in a particular technology area over time. This method can provide insights into the overall level of innovation in a particular area, as well as the trends and patterns in patent activity over time. | 100. Patent citation analysis: This method involves analyzing the citations between patents to identify the most influential patents in a particular area. This method can provide insights into the most important technologies and innovations in a particular area, as well as the influence and impact of different inventors and companies.

Sponsorship

Sponsorship Details

Statistical Methods Conferences warmly invite you to sponsor or exhibit of International Conference. We expect participants more than 200 numbers for our International conference will provide an opportunity to hear and meet/ads to Researchers, Practitioners, and Business Professionals to share expertise, foster collaborations, and assess rising innovations across the world in the core area of mechanical engineering.

Diamond Sponsorship

  1. Acknowledgment during the opening of the conference
  2. Complimentary Booth of size 10 meters square
  3. Four (4) delegate’s complimentary registrations with lunch
  4. Include marketing document in the delegate pack
  5. Logo on Conference website, Banners, Backdrop, and conference proceedings
  6. One exhibition stand (1×1 meters) for the conference
  7. One full cover page size ad in conference proceedings
  8. Opportunities for Short speech at events
  9. Option to sponsors conference kit
  10. Opportunity to sponsors conference lanyards, ID cards
  11. Opportunity to sponsors conference lunch
  12. Recognition in video ads
  13. 150-word company profile and contact details in the delegate pack

Platinum Sponsorship

  1. Three (3) delegate’s complimentary registrations with lunch
  2. Recognition in video ads
  3. Opportunity to sponsors conference lunch
  4. Opportunity to sponsors conference lanyards, ID cards
  5. Opportunity to sponsors conference kit
  6. Opportunity for Short speech at events
  7. One full-page size ad in conference proceedings
  8. One exhibition stand (1×1 meters) for the conference
  9. Logo on Conference website, Banners, Backdrop, and conference proceedings
  10. Include marketing document in the delegate pack
  11. Complimentary Booth of size 10 meters square
  12. Acknowledgment during the opening of the conference
  13. 100-word company profile and contact details in the delegate pack

Gold Sponsorship

  1. Two (2) delegate’s complimentary registrations with lunch
  2. Opportunities for Short speech at events
  3. Logo on Conference website, Banners, Backdrop, and conference proceedings
  4. Include marketing document in the delegate pack
  5. Complimentary Booth of size 10 meters square
  6. Acknowledgment during the opening of the conference
  7. 100-word company profile and contact details in the delegate pack
  8. ½ page size ad in conference proceedings

Silver Sponsorship

  1. Acknowledgment during the opening of the conference
  2. One(1) delegate’s complimentary registrations with lunch
  3. Include marketing document in the delegate pack
  4. Logo on Conference website, Banners, Backdrop, and conference proceedings
  5. ¼ page size ad in conference proceedings
  6. 100-word company profile and contact details in the delegate pack

Individual Sponsorship

  1. Acknowledgment during the opening of the conference
  2. One(1) delegate’s complimentary registrations with lunch

Registration Fees

Details Registration fees
Diamond Sponsorship USD 2999
Platinum Sponsorship USD 2499
Gold Sponsorship USD 1999
Silver Sponsorship USD 1499
Individual Sponsorship USD 999

Exhibitions

Exhibitions Details

Exhibit your Products & Services

Exhibit your Products & Services at Statistical Methods Conferences. Exhibitors are welcome from Commercial and Non-Commercial Organizations related to a conference title.

  • The best platform to develop new partnerships & collaborations.
  • Best location to speed up your route into every territory in the World.
  • Our exhibitor booths were visited 4-5 times by 80% of the attendees during the conference.
  • Network development with both Academia and Business.

Exhibitor Benefits

  • Exhibit booth of Size-3X3 sqm.
  • Promotion of your logo/Company Name/Brand Name through the conference website.
  • Promotional video on company products during the conference (Post session and Breaks).
  • Logo recognition in the Scientific program, Conference banner, and flyer.
  • One A4 flyer inserts into the conference kit.
  • An opportunity to sponsor 1 Poster Presentation Award.

Session Tracks

Conference Session Tracks

Introduction to statistics and probability | Descriptive statistics | Probability distributions | Estimation and hypothesis testing | Simple linear regression analysis | Multiple linear regression analysis | Analysis of variance (ANOVA) | Design of experiments (DOE) | Statistical process control (SPC) | Quality control and Six Sigma | Non-parametric statistical methods | Time series analysis | Bayesian statistics | Monte Carlo simulation | Reliability analysis | Introduction to statistical methods and data analysis | Descriptive statistics: measures of central tendency and dispersion | Probability theory and distributions | Hypothesis testing: one-sample and two-sample tests | Regression analysis: simple and multiple regression, model building, and diagnostics | Nonparametric methods: rank tests and correlation | Quality control: acceptance sampling, attribute control charts, and continuous improvement | Time series analysis: autocorrelation, trend analysis, and forecasting

Target Audience

Target Audience

    1. Researchers and Scientists
    2. Engineers and Technologists
    3. Industry Professionals
    4. Policymakers and Regulators
    5. Students and Early-Career Researchers

Target Universities

Target Universities

The target universities for an International Conference on Statistical Methods for Analyzing Engineering Data can vary based on the objectives and focus of the conference. However, some of the commonly targeted universities in the field of Statistical Methods for Analyzing Engineering Data include:

  1. Massachusetts Institute of Technology (MIT)
  2. University of Cambridge
  3. California Institute of Technology (Caltech)
  4. Stanford University
  5. University of California, Berkeley (UC Berkeley)
  6. National University of Singapore (NUS)
  7. Nanyang Technological University (NTU)
  8. Technical University of Munich (TUM)
  9. University of Tokyo
  10. University of Manchester

These universities are known for their strong research programs in the field of Statistical Methods for Analyzing Engineering Data and have produced numerous breakthroughs in the advancement of Statistical Methods for Analyzing Engineering Data. The conference can target researchers, students, and faculty from these universities to bring together the latest advancements and promote international collaboration in the field. The conference can also provide a platform for these universities to showcase their latest research and advancements, exchange ideas and form collaborations with other universities and research institutions in the field.

Target Companies

Target Companies

  1. General Electric (GE)
  2. Caterpillar Inc.
  3. Ford Motor Company
  4. Boeing
  5. Intel Corporation
  6. General Motors (GM)
  7. IBM Corporation
  8. Apple Inc.
  9. Microsoft Corporation
  10. Procter & Gamble Co.

Marketing Analysis

Marketing an  transnational conference on Statistical Methods for Analyzing Engineering Data in the field of Statistical Methods for Analyzing Engineering Data requires amulti-faceted approach to effectively reach implicit attendees and promote the event. Then are some strategies that can be effective in  selling such a conference   Targeted Marketing One effective marketing strategy is to target specific cult,  similar as experimenters, assiduity professionals, and  scholars. This can be done through targeted advertising and outreach to applicable associations and institutions.   Social Media exercising social media platforms can be an effective way to promote a conference and reach a wider  followership. Platforms  similar as Twitter, LinkedIn, and Facebook can be used to partake information about the conference and engage with implicit attendees.   Collaborations uniting with other associations and institutions can also help to promote a conference and reach a wider  followership. This can include partnering with assiduity associations, academic institutions, and government agencies.   Conference Website A well- designed and  stoner-friendly conference website can be an effective way to  give information to implicit attendees and promote the conference. The website should include information on the conference program, keynote speakers, and enrollment  details.   Dispatch juggernauts Dispatch  juggernauts can be an effective way to reach implicit attendees and  give them with information about the conference. This can include dispatch newsletters,  monuments, and follow- up emails after the conference.   Promotional Accoutrements Creating and distributing promotional accoutrements ,  similar as  pamphlets and  leaflets, can help to raise  mindfulness of the conference and reach a wider  followership.   Media Coverage Pursuing media content,  similar as press releases and interviews, can also help to promote the conference and reach a wider  followership.   Overall, a successful marketing strategy for a conference in the field of Statistical Methods for Analyzing Engineering Data will bear a combination of traditional and digital marketing strategies, precisely considering the target  followership and using a variety of channels to reach implicit attendees.

 

 

Renowned Speakers

We have invited most influential Speakers from around the world to give inspirational talks and workshops.

Key Features

Journal Publication | Conference Proceedings with ISBN  | Inspiring Speakers | Excellent Venue | Conference Kit | Certificate | Excellent Non Veg /Veg Buffet Lunch

Conference Awards

Best Presentation Awards | Best Poster Awards | Best Paper Awards

Conference Subject Tracks

Introduction to statistics and probability | Descriptive statistics | Probability distributions | Estimation and hypothesis testing | Simple linear regression analysis | Multiple linear regression analysis | Analysis of variance (ANOVA) | Design of experiments (DOE) | Statistical process control (SPC) | Quality control and Six Sigma | Non-parametric statistical methods | Time series analysis | Bayesian statistics | Monte Carlo simulation | Reliability analysis | Introduction to statistical methods and data analysis | Descriptive statistics: measures of central tendency and dispersion | Probability theory and distributions | Hypothesis testing: one-sample and two-sample tests | Regression analysis: simple and multiple regression, model building, and diagnostics | Nonparametric methods: rank tests and correlation | Quality control: acceptance sampling, attribute control charts, and continuous improvement | Time series analysis: autocorrelation, trend analysis, and forecasting

 

 

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