Comparison of Some Spike-and-Slab Priors for Bayesian Variable Selection in Multiple Linear Regression
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Date
2019
Journal Title
Journal ISSN
Volume Title
Publisher
Akamai University, U.S.A
Abstract
Variable selection has been a very essential challenge in building a multiple regression model. Exclusion of influential covariates or including covariate with zero effect will no doubt affect the estimation precision and as well the predictive accuracy of the model. “Spike-and-Slab prior” is an increasingly popular variable selection approach used in the Bayesian framework, which aids the variable selection and the estimation of regression parameters. In this research, the performances of the MCMC implementation for some versions of spike and slab priors for variable selection in normal linear regression models were investigated with regards to posterior inclusion probability for the simulated data under different setting (independent and correlated covariates, difference variance scales and varying sample sizes). Evidence from the simulation study revealed that the selected priors have similar performance under the independent setup and correlated setup, but the standard errors of coefficient estimates are higher for correlated covariates compare to independent covariates. The mean estimates of the coefficients get closer to the true coefficient values as the sample size increases under all different priors considered, and also the posterior inclusion probability depends on the size of variance of the slab component.
Description
Keywords
Covariates,Posterior distribution, Precision, Regression coefficients, Spike-and-slab priors
Citation
Pacific Journal of Science and Technology