Bayesian Screening of Covariates in Linear Regression Models Using Correlation Thresholds

Roberta Paroli, Ioannis Ntzoufras

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

In this work, we propose a fast and simple Bayesian method based on simple and partial correlation coefficients to identify covariates which are not supported in terms of the Bayes Factors in normal linear regression models. By this way, when the number of the covariates is large, we can screen out the covariates with negligible effects and reduce the size of the model space in such a way that we can implement traditional Bayesian variable selection methods.We focus on the g-prior implementation where computations are exact but the approach is general and can be easily extended to any prior setup. The proposed method is illustrated using simulation studies.
Original languageEnglish
Title of host publicationBOOK OF SHORT PAPERS – SIS2021
Pages1232-1237
Number of pages6
Publication statusPublished - 2021
Event50TH MEETING OF THE ITALIAN STATISTICAL SOCIETY - Siena
Duration: 21 Jun 202125 Jun 2021

Conference

Conference50TH MEETING OF THE ITALIAN STATISTICAL SOCIETY
CitySiena
Period21/6/2125/6/21

Keywords

  • Bayes Factor
  • simple and partial correlation coefficient
  • g-prior
  • Bayesian Variable selection

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