On the small sample behavior of Dirichlet process mixture models for data supported on compact intervals
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Date
2019
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Abstract
Bayesian nonparametric models provide a general framework for flexible statistical modeling of modern complex data sets. We compare a rate-optimal and rate-suboptimal Bayesian nonparametric model for density estimation for data supported on a compact interval, by means of the analyses of simulated and real data. The results show that rate-optimal models are not uniformly better, across sample sizes, with respect to the way in which the posterior mass concentrates around a true model and that suboptimal models can outperform the optimal ones, even for relatively large sample sizes.
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Keywords
Density estimation, Random Bernstein polynomials, Mixture of beta distributions, Bayesian nonparametrics, Posterior convergence rate