Variable Selection in Bayesian Semiparametric Regression Models

TL;DR

This paper extends Bayesian variable selection methods for Gaussian process regression to jointly select regression terms and spatial covariates, enhancing prediction accuracy through posterior probability-based model comparison.

Contribution

It introduces a novel approach for simultaneous selection of regression components and spatial correlation covariates in Bayesian Gaussian process models.

Findings

Improved prediction accuracy via posterior probability model selection.

Effective joint selection of regression terms and spatial covariates.

Enhanced model interpretability in spatial regression contexts.

Abstract

In this paper we extend existing Bayesian methods for variable selection in Gaussian process regression, to select both the regression terms and the active covariates in the spatial correlation structure. We then use the estimated posterior probabilities to choose between relatively few modes through cross-validation, and consequently improve prediction.