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Entropies and rates of convergence for maximum likelihood and Bayes estimation for mixtures of normal densities. (English) Zbl 1043.62025

Summary: We study the rates of convergence of the maximum likelihood estimator (MLE) and posterior distribution in density estimation problems, where the densities are location or location-scale mixtures of normal distributions with the scale parameter lying between two positive numbers. The true density is also assumed to lie in this class with the true mixing distribution either compactly supported or having sub-Gaussian tails. We obtain bounds for Hellinger bracketing entropies for this class, and from these bounds, we deduce the convergence rates of (sieve) MLEs in the Hellinger distance. The rate turns out to be \((\log n)^\kappa /\sqrt{n}\), where \(\kappa \geq 1\) is a constant that depends on the type of mixtures and the choice of the sieve. Next, we consider a Dirichlet mixture of normals as a prior on the unknown density. We estimate the prior probability of a certain Kullback-Leibler type neighborhood and then invoke a general theorem that computes the posterior convergence rate in terms the growth rate of the Hellinger entropy and the concentration rate of the prior. The posterior distribution is also seen to converge at the rate \((\log n)^\kappa /\sqrt{n}\), where \(\kappa\) now depends on the tail behavior of the base measure of the Dirichlet process.

MSC:

62G07 Density estimation
62F12 Asymptotic properties of parametric estimators
62F15 Bayesian inference
62B10 Statistical aspects of information-theoretic topics
62G20 Asymptotic properties of nonparametric inference
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