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December 2010 Adaptive nonparametric Bayesian inference using location-scale mixture priors
R. de Jonge, J. H. van Zanten
Ann. Statist. 38(6): 3300-3320 (December 2010). DOI: 10.1214/10-AOS811

Abstract

We study location-scale mixture priors for nonparametric statistical problems, including multivariate regression, density estimation and classification. We show that a rate-adaptive procedure can be obtained if the prior is properly constructed. In particular, we show that adaptation is achieved if a kernel mixture prior on a regression function is constructed using a Gaussian kernel, an inverse gamma bandwidth, and Gaussian mixing weights.

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R. de Jonge. J. H. van Zanten. "Adaptive nonparametric Bayesian inference using location-scale mixture priors." Ann. Statist. 38 (6) 3300 - 3320, December 2010. https://doi.org/10.1214/10-AOS811

Information

Published: December 2010
First available in Project Euclid: 20 September 2010

zbMATH: 1204.62062
MathSciNet: MR2766853
Digital Object Identifier: 10.1214/10-AOS811

Subjects:
Primary: 62C10 , 62G08
Secondary: 62G20

Keywords: Adaptation , Bayesian inference , kernel mixture priors , Nonparametric regression , posterior distribution , rate of convergence

Rights: Copyright © 2010 Institute of Mathematical Statistics

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Vol.38 • No. 6 • December 2010
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