Bayesian Screening of Covariates in Linear Regression Models Using Correlation Thresholds

Roberta Paroli, Ioannis Ntzoufras

Risultato della ricerca: Contributo in libroContributo a convegno

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.
Lingua originaleEnglish
Titolo della pubblicazione ospiteBOOK OF SHORT PAPERS – SIS2021
Pagine1232-1237
Numero di pagine6
Stato di pubblicazionePubblicato - 2021
Evento50TH MEETING OF THE ITALIAN STATISTICAL SOCIETY - Siena
Durata: 21 giu 202125 giu 2021

Convegno

Convegno50TH MEETING OF THE ITALIAN STATISTICAL SOCIETY
CittàSiena
Periodo21/6/2125/6/21

Keywords

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

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