Posterior consistency of Gaussian process prior for nonparametric binary regression

The Annals of Statistics · 2006 · Volume 34 · Issue 5

Abstract

Consider binary observations whose response probability is an unknown smooth function of a set of covariates. Suppose that a prior on the response probability function is induced by a Gaussian process mapped to the unit interval through a link function. In this paper we study consistency of the resulting posterior distribution. If the covariance kernel has derivatives up to a desired order and the bandwidth parameter of the kernel is allowed to take arbitrarily small values, we show that the posterior distribution is consistent in the L1-distance. As an auxiliary result to our proofs, we show that, under certain conditions, a Gaussian process assigns positive probabilities to the uniform neighborhoods of a continuous function. This result may be of independent interest in the literature for small ball probabilities of Gaussian processes.

Keywords

62G08, 62G20, binary regression, Gaussian process, Karhunen–Loeve expansion, maximal inequality, posterior consistency, ‎reproducing kernel Hilbert ‎space

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