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

S-M Chang1, J-Y Tzeng2, R-B Chen1

  • 1Department of Statistics, National Cheng Kung University, Tainan, Taiwan.

Journal of Statistical Computation and Simulation
|October 28, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a Bayesian screening procedure for analyzing high-throughput biological data. The method efficiently identifies important covariates in binary response models, offering improved performance with comparable computational cost, especially in small sample sizes.

Keywords:
Logistic regressionProbit regressionSure independence screeningg-prior

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Area of Science:

  • Statistics
  • Bioinformatics
  • Computational Biology

Background:

  • Screening procedures are crucial for analyzing large biological datasets with numerous covariates.
  • Existing methods often focus on marginal covariate information, which can be limiting.
  • High-throughput studies frequently face scenarios where covariates outnumber subjects.

Purpose of the Study:

  • To introduce a novel Bayesian screening procedure for binary response models.
  • To develop a method that simultaneously models all covariates.
  • To provide a computationally efficient alternative to existing screening techniques.

Main Methods:

  • The proposed method utilizes a Bayesian approach for screening covariates in logit and probit models.
  • It employs posterior means of regression coefficients as screening statistics.
  • A generalized g-prior is imposed, enabling analytical derivation of posterior means without Markov chain Monte Carlo (MCMC) implementation.

Main Results:

  • The Bayesian screening procedure offers closed-form statistics for efficient covariate assessment.
  • Simulations and real data analysis demonstrate the method's utility.
  • Improved performance is observed in small sample size scenarios with comparable computational expense.

Conclusions:

  • The developed Bayesian screening procedure is effective for high-throughput biological data analysis.
  • It provides an efficient and accurate method for covariate selection in binary models.
  • The approach is particularly beneficial when dealing with limited sample sizes.