Fast Bayesian variable screenings for binary response regressions with small sample size

研究成果: Article同行評審

1 引文 斯高帕斯(Scopus)

摘要

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.

原文English
頁(從 - 到)2708-2723
頁數16
期刊Journal of Statistical Computation and Simulation
87
發行號14
DOIs
出版狀態Published - 2017 九月 22

All Science Journal Classification (ASJC) codes

  • 統計與概率
  • 建模與模擬
  • 統計、概率和不確定性
  • 應用數學

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