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.
Citation
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
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