Convergence rates for density estimation with Bernstein polynomials

Abstract

Mixture models for density estimation provide a very useful set up for the Bayesian or the maximum likelihood approach.For a density on the unit interval, mixtures of beta densities form a flexible model. The class of Bernstein densities is a much smaller subclass of the beta mixtures defined by Bernstein polynomials, which can approximate any continuous density. A Bernstein polynomial prior is obtained by putting a prior distribution on the class of Bernstein densities. The posterior distribution of a Bernstein polynomial prior is consistent under very general conditions. In this article, we present some results on the rate of convergence of the posterior distribution. If the underlying distribution generating the data is itself a Bernstein density, then we show that the posterior distribution converges at “nearly parametric rate” $(log n) /\sqrt{n}$ for the Hellinger distance. If the true density is not of the Bernstein type, we show that the posterior converges at a rate $n^{1/3}(log n)^{5/6}$ provided that the true density is twice differentiable and bounded away from 0. Similar rates are also obtained for sieve maximum likelihood estimates.These rates are inferior to the pointwise convergence rate of a kernel type estimator.We show that the Bayesian bootstrap method gives a proxy for the posterior distribution and has a convergence rate at par with that of the kernel estimator.