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Activity Number: 260 - SPEED: Topics in Bayesian Analysis
Type: Contributed
Date/Time: Monday, July 30, 2018 : 3:05 PM to 3:50 PM
Sponsor: Section on Bayesian Statistical Science
Abstract #332814
Title: Variable Selection with Missing Data Imputation in the High-Dimensional Setting
Author(s): Soeun Kim* and Yunxi Zhang
Companies: The University of Texas Health Science Center at Houston and The University of Texas Health Science Center at Houston
Keywords: Missing Data; Imputation; Variable Selection; Shrinkage Prior; High dimensional

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. We propose approaches 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 are presented and compared with other possible approaches.

Authors who are presenting talks have a * after their name.

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