B0813
Title: Nonparametric goodness of fit via cross-validation Bayes factors
Authors: Jeff Hart - Texas AM University (United States) [presenting]
Taeryon Choi - Korea University (Korea, South)
Abstract: A nonparametric Bayes procedure is proposed for testing the fit of a parametric model for a distribution. Alternatives to the parametric model are kernel density estimates. Data splitting makes it possible to use kernel estimates for this purpose in a Bayesian setting. A kernel estimate indexed by bandwidth is computed from one part of the data, a training set, and then used as a model for the rest of the data, a validation set. A Bayes factor is calculated from the validation set by comparing the marginal for the kernel model with the marginal for the parametric model of interest. A simulation study is used to investigate how large the training set should be, and examples involving astronomy and wind data are provided. Evidence for Bayes consistency of the proposed test is also given.
