Longitudinal studies are widely conducted in public health research. It is common that longitudinal studies collect a large number of covariates, some of which are unimportant in explaining the response. Moreover, longitudinal data analysis is challenged by the presence of measurement error and missing observations. In this talk, I will describe a variable selection procedure that handle high dimensional longitudinal data with "missingness" and measurement error, and assess its validity using simulation studies, and demonstrate its application using an aging study example.
Speaker Biography - Xianming Tan, Ph.D.
Xianming Tan, Ph.D.
Department of Biostatistics and Lineberger Comprehensive Cancer Center, Gillings School of Global Public Health, UNC-Chapel Hill
Dr. Xianming Tan is a Research Associate Professor in the Department of Biostatistics and Lineberger Comprehensive Cancer Center, Gillings School of Global Public Health at the University of North Carolina at Chapel Hill. He earned his Ph.D. degree in Statistics from Nankai University, China, followed by postdoctoral research in Biostatistics at the Queen’s University, Canada, and Pennsylvania State University. Before joining UNC, he was a consultant Biostatistician at the McGill University Health Centre. His research interests include finite mixture models, design of clinical trials, and longitudinal data analysis.
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