Bayesian model selection: a predictive approach with losses based on distances L1 and L2

Julián de la Horra and María Teresa Rodríguez-Bernal

Statistics & Probability Letters, 2005, vol. 71, issue 3, 257-265

Abstract: A Bayesian model consists of two elements: a sampling model and a prior density. In this paper, we propose a new predictive approach for selecting a Bayesian model through a decision problem. The key idea in the paper is the loss function; we propose to measure the L1 distance (or the squared L2 distance) between the densities we can use for predicting future observations: sampling densities and posterior predictive densities. The method is also applied to the problem of variable selection in a regression model, showing a good behavior.

Keywords: Posterior; predictive; density; Posterior; expected; loss; L1; distance; L2; distance; Bayesian; model; selection; Variable; selection (search for similar items in EconPapers)
Date: 2005
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