Asymptotic properties of posterior distributions in nonparametric regression with non-Gaussian errors
- Authors
- Choi, Taeryon
- Issue Date
- Dec-2009
- Publisher
- SPRINGER HEIDELBERG
- Keywords
- Posterior consistency; Uniformly consistent tests; Kullback-Leibler divergence; Hellinger metric; Prior positivity; Symmetric density
- Citation
- ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS, v.61, no.4, pp 835 - 859
- Pages
- 25
- Indexed
- SCIE
SCOPUS
- Journal Title
- ANNALS OF THE INSTITUTE OF STATISTICAL MATHEMATICS
- Volume
- 61
- Number
- 4
- Start Page
- 835
- End Page
- 859
- URI
- https://scholar.korea.ac.kr/handle/2021.sw.korea/118884
- DOI
- 10.1007/s10463-008-0168-2
- ISSN
- 0020-3157
1572-9052
- Abstract
- We investigate the asymptotic behavior of posterior distributions in nonparametric regression problems when the distribution of noise structure of the regression model is assumed to be non-Gaussian but symmetric such as the Laplace distribution. Given prior distributions for the unknown regression function and the scale parameter of noise distribution, we show that the posterior distribution concentrates around the true values of parameters. Following the approach by Choi and Schervish (Journal of Multivariate Analysis, 98, 1969-1987, 2007) and extending their results, we prove consistency of the posterior distribution of the parameters for the nonparametric regression when errors are symmetric non-Gaussian with suitable assumptions.
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Collections - College of Political Science & Economics > Department of Statistics > 1. Journal Articles
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