On the small sample behavior of Dirichlet process mixture models for data supported on compact intervals. Issue 3 (26th February 2021)

• Record Type: Journal Article
• Authors: Wehrhahn, Claudia; Jara, Alejandro; Barrientos, Andrés F.
• Is Part Of: Communications in statistics. Volume 50:Issue 3(2021)
• Page Start: 786
• Page End: 810
• Publication Date: 2021-02-26
• Subjects: Density estimation -- Random Bernstein polynomials -- Mixture of beta distributions -- Bayesian nonparametrics -- Posterior convergence rate; Mathematical statistics -- Periodicals; Mathematical statistics -- Data processing -- Periodicals; Digital computer simulation -- Periodicals; 519.5
• DOI: 10.1080/03610918.2019.1568470
• Languages: English
• ISSNs: 0361-0918

Abstract

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.