- Bayesian Anal.
- Volume 12, Number 3 (2017), 653-677.
Nonparametric Goodness of Fit via Cross-Validation Bayes Factors
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. A proof of Bayes consistency of the proposed test is also provided.
Bayesian Anal., Volume 12, Number 3 (2017), 653-677.
First available in Project Euclid: 17 August 2016
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Hart, Jeffrey D.; Choi, Taeryon. Nonparametric Goodness of Fit via Cross-Validation Bayes Factors. Bayesian Anal. 12 (2017), no. 3, 653--677. doi:10.1214/16-BA1018. https://projecteuclid.org/euclid.ba/1471454532