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

The Birnbaum-Saunders (BS) distribution is broadly used to model failure times in reliability and survival analysis. In this article, we propose a simultaneous parameter estimation and variable selection procedure in a log-linear BS regression model for high-dimensional survival data. To deal with censored survival data, we iteratively run a combination of the stochastic EM algorithm (SEM) and variable selection procedure to generate pseudo-complete data and select variables until convergence. Treating pseudo-complete data as uncensored data via SEM makes it possible to incorporate iterative penalized least squares and simplify computation. We demonstrate the efficacy of our method using simulated and real datasets.