Fast Bayesian Variable Screenings for Binary Response Regressions with Small Sample Size.

TitleFast Bayesian Variable Screenings for Binary Response Regressions with Small Sample Size.
Publication TypeJournal Article
Year of Publication2017
AuthorsChang, S-M, J-Y Tzeng, and R-B Chen
JournalJ Stat Comput Simul
Date Published2017

Screening procedures play an important role in data analysis, especially in high-throughput biological studies where the datasets consist of more covariates than independent subjects. In this article, a Bayesian screening procedure is introduced for the binary response models with logit and probit links. In contrast to many screening rules based on marginal information involving one or a few covariates, the proposed Bayesian procedure simultaneously models all covariates and uses closed-form screening statistics. Specifically, we use the posterior means of the regression coefficients as screening statistics; by imposing a generalized g-prior on the regression coefficients, we derive the analytical form of their posterior means and compute the screening statistics without Markov chain Monte Carlo implementation. We evaluate the utility of the proposed Bayesian screening method using simulations and real data analysis. When the sample size is small, the simulation results suggest improved performance with comparable computational cost.

Alternate JournalJ Stat Comput Simul
Original PublicationFast Bayesian variable screenings for binary response regressions with small sample size.
PubMed ID29075047
PubMed Central IDPMC5653235
Grant ListP01 CA142538 / CA / NCI NIH HHS / United States