Variable Selection in Log-linear Birnbaum-Saunders Regression Model for High-dimensional Survival Data via the Elastic Net and Stochastic EM

Abstract: Birnbaum-Saunders (BS) distribution is broadly used to model failure times in reliability and survival analysis. In this paper, we propose a variable selection procedure in a log-linear BS regression model for high-dimensional survival data. We introduce a path wise algorithm via cyclical coordinate descent based on the elastic net penalty. To deal with censored survival data, we iteratively run a combination of stochastic EM algorithm (StEM) and variable selection procedure to generate pseudo-complete data and select variables until convergence. Treating pseudo-complete data as uncensored data via StEM simplifies computation and makes it possible to incorporate iterative least squares for parameter estimation and variable selection simultaneously. We demonstrate the efficacy of our method using simulated and real data sets.

Location:

MS 431 Math Science Building

Speaker:

Yukun Zhang, Departments of Mathematics and Statistics, University of Calgary

More information at http://www.ucalgary.ca/events/calendar/variable-selection-log-linear-birnbaum-saunders-regression-model-high-dimensional-survival