Bayesian cross-validation for model evaluation and selection, with application to the North American Breeding Bird Survey

Ecology
By:  and 

Links

Abstract

The analysis of ecological data has changed in two important ways over the last 15 years. The development and easy availability of Bayesian computational methods has allowed and encouraged the fitting of complex hierarchical models. At the same time, there has been increasing emphasis on acknowledging and accounting for model uncertainty. Unfortunately, the ability to fit complex models has outstripped the development of tools for model selection and model evaluation: familiar model selection tools such as Akaike's information criterion and the deviance information criterion are widely known to be inadequate for hierarchical models. In addition, little attention has been paid to the evaluation of model adequacy in context of hierarchical modeling, i.e., to the evaluation of fit for a single model. In this paper, we describe Bayesian cross-validation, which provides tools for model selection and evaluation. We describe the Bayesian predictive information criterion and a Bayesian approximation to the BPIC known as the Watanabe-Akaike information criterion. We illustrate the use of these tools for model selection, and the use of Bayesian cross-validation as a tool for model evaluation, using three large data sets from the North American Breeding Bird Survey.

Additional publication details

Publication type Article
Publication Subtype Journal Article
Title Bayesian cross-validation for model evaluation and selection, with application to the North American Breeding Bird Survey
Series title Ecology
DOI 10.1890/15-1286.1
Volume 97
Issue 7
Year Published 2016
Language English
Publisher Ecological Society of America
Publisher location Washington, D.C.
Contributing office(s) Patuxent Wildlife Research Center
Description 13 p.
First page 1746
Last page 1758