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Activity Number: 48 - New Frontiers in High-Dimensional and Complex Data analyses
Type: Invited
Date/Time: Sunday, July 28, 2019 : 4:00 PM to 5:50 PM
Sponsor: Biometrics Section
Abstract #300494
Title: Penalized Empirical Likelihood for the Sparse Cox Model
Author(s): Dongliang Wang and Tong Tong Wu and Yichuan Zhao*
Companies: SUNY Upstate Medical University and University of Rochester and Georgia State University
Keywords: Coordinate descent algorithm; high-dimensional data; oracle property; penalized likelihood; proportional hazards model; right censoring

The current penalized regression methods for selecting predictor variables and estimating the associated regression coefficients in the Cox model are mainly based on partial likelihood. In this paper, an empirical likelihood method is proposed for the Cox model in conjunction with appropriate penalty functions when the dimensionality of data is high. Theoretical properties of the resulting estimator for the large sample are proved. Simulation studies suggest that empirical likelihood works better than partial likelihood in terms of selecting correct predictors without introducing more model errors. The well-known primary biliary cirrhosis data set is used to illustrate the proposed empirical likelihood method.

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

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