Adaptive nonparametric Bayesian inference using location-scale mixture priors
Statistics Theory
2012-11-12 v1 Statistics Theory
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.
Cite
@article{arxiv.1211.2121,
title = {Adaptive nonparametric Bayesian inference using location-scale mixture priors},
author = {R. de Jonge and J. H. van Zanten},
journal= {arXiv preprint arXiv:1211.2121},
year = {2012}
}
Comments
Published in at http://dx.doi.org/10.1214/10-AOS811 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org)