On several estimates to the precision parameter of Dirichlet process prior

ABSTRACT

The article presents careful comparisons among several empirical Bayes estimates to the precision parameter of Dirichlet process prior, with the setup of univariate observations and multigroup data. Specifically, the data are equipped with a two-stage compound sampling model, where the prior is assumed as a Dirichlet process that follows within a Bayesian nonparametric framework. The precision parameter α measures the strength of the prior belief and kinds of estimates are generated on the basis of observations, including the naive estimate, two calibrated naive estimates, and two different types of maximum likelihood estimates stemming from distinct distributions. We explore some theoretical properties and provide explicitly detailed comparisons among these estimates, in the perspectives of bias, variance, and mean squared error. Besides, we further present the corresponding calculation algorithms and numerical simulations to illustrate our theoretical achievements.

KEYWORDS:

• Bayesian nonparametrics
• Calibrated estimates
• Dirichlet process prior
• Maximum likelihood estimate
• Precision parameter
• Yule-Simon distribution

MATHEMATICS SUBJECT CLASSIFICATION:

• Primary classification 62 Statistics
• Secondary classification 62G05 Estimation