Adaptive nonparametric Bayesian inference using location-scale mixture priors

R. de Jonge; J. H. van Zanten

doi:10.1214/10-aos811

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arXiv · 1211.2121

Adaptive nonparametric Bayesian inference using location-scale mixture priors

R. de Jonge·J. H. van Zanten

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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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BibTeX RIS

R. de Jonge, J. H. van Zanten. 2012-11-09. Adaptive nonparametric Bayesian inference using location-scale mixture priors. https://doi.org/10.1214/10-aos811

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