JOURNAL OF SHANDONG UNIVERSITY(NATURAL SCIENCE) ›› 2021, Vol. 56 ›› Issue (12): 84-93.doi: 10.6040/j.issn.1671-9352.0.2021.065

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A Bayesian approach for variable selection using spike-and-slab prior distribution

ZHANG Xian-you, LI Dong-xi*   

  1. College of Mathematics, Taiyuan University of Technology, Taiyuan 030024, Shanxi, China
  • Published:2021-11-25

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Abstract: For ultra-high dimensional data, a Bayesian approach using a novel spike-and-slab prior for variable selection in high-dimensional linear regression models is presented. The proposed method aims to inherit the advantages of the elastic net and the EM algorithm to obtain sparse prediction models with faster convergence speed. Furthermore, a spike-and-slab setting of coefficients which allows for borrowing strength across coordinates, adjust to data sparsity information and exert multiplicity adjustment is proposed. Finally, the accuracy and efficiency of the proposed method are demonstrated via comparisons and analyses with common methods.

Key words: variable selection, high dimensional, spike-and-slab prior distribution, elastic net, sparse model

CLC Number: 

  • O212.1
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