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A note on Bayes factor consistency in partial linear models

Authors
Choi, TaeryonRousseau, Judith
Issue Date
11월-2015
Publisher
ELSEVIER SCIENCE BV
Keywords
Bayes factor; Consistency; Fourier series; Gaussian processes; Hellinger distance; Kullback-Leibler neighborhoods; Lack of fit testing
Citation
JOURNAL OF STATISTICAL PLANNING AND INFERENCE, v.166, pp.158 - 170
Indexed
SCIE
SCOPUS
Journal Title
JOURNAL OF STATISTICAL PLANNING AND INFERENCE
Volume
166
Start Page
158
End Page
170
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/92022
DOI
10.1016/j.jspi.2015.03.009
ISSN
0378-3758
Abstract
It has become increasingly important to understand the asymptotic behavior of the Bayes factor for model selection in general statistical models. In this paper, we discuss recent results on Bayes factor consistency in semiparametric regression problems where observations are independent but not identically distributed. Specifically, we deal with the model selection problem in the context of partial linear models in which the regression function is assumed to be the additive form of the parametric component and the nonparametric component using Gaussian process priors, and Bayes factor consistency is investigated for choosing between the parametric model and the semiparametric alternative. (C) 2015 Elsevier B.V. All rights reserved.
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Choi, Tae ryon
정경대학 (통계학과)
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