Nonparametric Goodness of Fit via Cross-Validation Bayes Factors
- Authors
- Hart, Jeffrey D.; Choi, Taeryon
- Issue Date
- 9월-2017
- Publisher
- INT SOC BAYESIAN ANALYSIS
- Keywords
- bandwidth selection; Bayes factor; consistency; cross validation; goodness-of-fit tests; kernel density estimates
- Citation
- BAYESIAN ANALYSIS, v.12, no.3, pp.653 - 677
- Indexed
- SCIE
SCOPUS
- Journal Title
- BAYESIAN ANALYSIS
- Volume
- 12
- Number
- 3
- Start Page
- 653
- End Page
- 677
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/82423
- DOI
- 10.1214/16-BA1018
- ISSN
- 1936-0975
- 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. A proof of Bayes consistency of the proposed test is also provided.
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Collections - College of Political Science & Economics > Department of Statistics > 1. Journal Articles
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