Superiority of Bayes estimators over the MLE in high dimensional multinomial models and its implication for nonparametric Bayes theory

Rabi Bhattacharya, Rachel Oliver

Research output: Contribution to journalArticlepeer-review

Abstract

The performance of Bayes estimators is examined, in comparison with the MLE, in multinomial models with a relatively large number of cells. The prior for the Bayes estimator is taken to be the conjugate Dirichlet, i.e., the multivariate Beta, with exchangeable distributions over the coordinates, including the non-informative uniform distribution. The choice of the multinomial is motivated by its many applications in business and industry, but also by its use in providing a simple nonparametric estimator of an unknown distribution. It is striking that the Bayes procedure outperforms the asymptotically efficient MLE over most of the parameter spaces for even moderately large dimensional parameter spaces and rather large sample sizes.

Original languageEnglish (US)
Article number107011
JournalComputational Statistics and Data Analysis
Volume150
DOIs
StatePublished - Oct 2020

Keywords

  • Bayes estimators versus the MLE
  • High dimensional multinomials
  • Nonparametric Bayes

ASJC Scopus subject areas

  • Statistics and Probability
  • Computational Mathematics
  • Computational Theory and Mathematics
  • Applied Mathematics

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