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Biostatistics Seminar Series: Hosting Dr. Yunxi Zhang of UMMC, with her seminar title “Variable Selection and Imputation for High-Dimensional Incomplete Data”


Dear Colleagues,

The Division of Biostatistics at the Department of Preventive Medicine invites you to attend the following seminar by our guest speaker, Dr. Yunxi Zhang of the University of Mississippi Medical Center .

Date: Monday, March 9th, 2020

Time: 2 P.M.

Place: 4th Floor Conference Room 400 in the Doctors Office Building at 66 N. Pauline Street, Memphis, TN 38105. Please park in the multi-story parking garage adjacent to the Doctors Office Building, and bring your parking card with you so we can give you a validation ticket.

ZOOM Virtual Room Connection: If you cannot join us in person, please join us in our virtual conference room at  https://tennessee.zoom.us/j/476519327

Presenter: Dr. Yunxi Zhang of UMMC.

Title:Variable Selection and Imputation for High-Dimensional Incomplete Data

Abstract: Missing data are an inevitable problem in data with a large number of variables. The presence of missing data obstructs the implementation of the existing variable selection methods. This is especially an issue when there is a limited number of observations. Applicable and efficient selection with imputation method is necessary to obtain valid results. In this talk, I will propose an approach to efficiently select important variables from high dimensional data in the presence of missing data. We employ the shrinkage prior and multiple imputation for variable selection in the high-dimensional setting with missing values. Simulation study and analysis results will be presented and compared with other possible approaches.

About the speaker:
Dr. Yunxi Zhang is an instructor in the Department of Data Science at University of Mississippi Medical Center (UMMC). She also served as the biostatistician of the Center for Telehealth at UMMC. She has been involved in a number of consulting and collaborative researches with experience in performing statistical analysis including longitudinal data analysis, generalized linear mixed models, survival data analysis, etc, to different types of research studies. Her main research interest is in developing statistical methods for analyzing incomplete data.

We look forward to seeing you all among us. 

Chi-Yang Chiu, Ph.D.

Assistant Professor of Biostatistics

Department of Preventive Medicine, UTHSC