Dr Roma Jandarov | Wilcoxon signed rank test

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Dr Roma Jandarov | Leading Researcher in Wilcoxon signed rank test

Congratulations,Dr Roma Jandarov on winning the esteemed Best Researcher Award  from ResearchW! Your dedication, innovative research, and scholarly contributions have truly made a significant impact in your field. Your commitment to advancing knowledge and pushing the boundaries of research is commendable. Here’s to your continued success in shaping the future of academia and making invaluable contributions to your field. Well done!

Dr Roma Jandarov Sebastin is a distinguished academic and researcher in the field of renewable energy, holds a PhD in Biosystems Engineering from Kangwon National University, South Korea. His academic journey has been marked by a profound dedication to advancing solar energy technologies, specifically in solar thermal harvesting and its integration into agricultural and architectural applications.

Professional Profiles:
Education

PhD in, statistics ,2007-2012

Position: Research Assistant

Department: Biostatistics

Duties: Contributed to method development at UW Center for Clear Air Research, focusing on cardiovascular health effects of air pollution with Professors Adam Szpiro, Lianne Sheppard, Paul Sampson.

Pennsylvania State University, University Park, Pennsylvania, USA

Years: 2009 – 2012

Position: Graduate Lecturer

Department: Statistics

Duties: Worked on statistical inference for gravity time series SIR models for disease dynamics as part of a Bill and Melinda Gates Foundation project with Professors Murali Haran and Ottar Bjornstad.

Years: 2009 – 2010

Position: Graduate Teaching Assistant

Department: Statistics

Duties: Taught statistics courses.

Years: 2007 – 2009

Position: Teaching Assistant

Department: Statistics

Duties: Assisted instructors in preparing and teaching statistics courses.

Publications Top Note :

 Assessment of indoor bioaerosol exposure using direct-reading versus traditional methods-potential application to home health care.  

 A Novel Simulator for Teaching Endobronchial Ultrasound-guided Needle Biopsy.   paper published in 2023 Jul

 Complex Morphologic Analysis of Cerebral Aneurysms Through the Novel Use of Fractal Dimension as a Predictor of Rupture Status: A Proof of Concept Study.   paper published in 2023 Jul

 Durvalumab plus Cetuximab in Patients with Recurrent or Metastatic Head and Neck Squamous Cell Carcinoma: An Open-label, Nonrandomized, Phase II Clinical Trial    paper published in 2023 May

 Upregulation of acid ceramidase contributes to tumor progression in tuberous sclerosis complex.   paper published in 2023 May

 The Associations of Trans-3′-Hydroxy Cotinine, Cotinine, and the Nicotine Metabolite Ratio in Pediatric Patients with Tobacco Smoke Exposure   paper published in 2023 Apr

 Sources of Tobacco Smoke Exposure and Their Associations With Serum Cotinine Levels Among US Children and Adolescents  paper published in 2023 Apr

 A Hands-Free, Oral Positive Expiratory Pressure Device for Exertional Dyspnea and Desaturation in COPD.     paper published in 2022 Sep 23

 Distinguishing Exposure to Secondhand and Thirdhand Tobacco Smoke among U.S. Children Using Machine Learning: NHANES 2013-2016   paper published in 2023 Feb 7

 Carcinogenic and tobacco smoke-derived particulate matter biomarker uptake and associated healthcare patterns among children.  paper published in 2023 Jan

 

 

Nonparametric methods

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Nonparametric methods

This conference serves as a platform for sharing cutting-edge research and practical applications of statistical methods in engineering data analysis. It fosters collaboration and knowledge exchange in the pursuit of enhancing the quality and reliability of engineering systems.

Kernel Density Estimation (KDE) for Reliability Assessment

Explore the application of kernel density estimation techniques to assess the reliability of engineering systems by modeling failure and repair times, enabling more informed decision-making in maintenance and operations.

Nonparametric Regression for Quality Control

Investigate the use of nonparametric regression models, such as loess and spline methods, in quality control processes to detect and address variations in manufacturing and production systems, ensuring product consistency.

Survival Analysis for Engineering Systems

Delve into survival analysis methods, such as Kaplan-Meier estimation and Cox proportional hazards models, to analyze time-to-event data in engineering contexts, such as equipment lifetimes and component failures.

Nonparametric Hypothesis Testing in Experimental Design

Examine the application of nonparametric tests like the Wilcoxon rank-sum test and the Kruskal-Wallis test to assess the significance of treatment effects and factors in engineering experiments, facilitating robust conclusions.

Functional Data Analysis for Sensor Data

Explore the use of functional data analysis techniques to analyze and model high-dimensional sensor data generated by complex engineering systems, enabling real-time monitoring and anomaly detection.

Non-parametric statistical methods

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Non-parametric statistical methods

The International Conference on Statistical Methods for Analyzing Engineering Data (ICSMAED) is a prestigious event that brings together leading experts, researchers, and practitioners from the field of engineering data analysis. This conference serves as a platform for the exchange of knowledge, ideas, and cutting-edge research in the realm of statistical methods applied to engineering data.

Design of Experiments (DoE) in Engineering

This subtopic delves into the application of experimental design techniques to optimize and enhance engineering processes, ensuring efficient utilization of resources and improved product performance.

Reliability Analysis in Engineering

Reliability assessment techniques, such as Weibull analysis and accelerated life testing, are discussed to ensure that engineering systems meet high standards of performance and durability.

Statistical Process Control (SPC) in Manufacturing

SPC methods play a crucial role in maintaining product quality and process efficiency in engineering manufacturing, and this subtopic explores the latest advancements in this domain.

Regression Analysis for Engineering Applications

Regression models are widely employed in engineering to analyze relationships between variables and predict outcomes. This subtopic focuses on innovative regression techniques tailored for engineering data.

Bayesian Methods in Engineering Data Analysis

Bayesian statistical methods offer a powerful framework for handling uncertainty in engineering data, making informed decisions, and updating models with new information. Discussions in this subtopic revolve around Bayesian applications specific to engineering contexts.