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

TRIPODS Seminar

Superiority of Bayes estimators over the MLE in high dimensional multinomial models and its implication for nonparametric Bayes theory
Series: TRIPODS Seminar
Location: ENR2 S210
Presenter: Rachel Oliver, Math PhD student at the University of Arizona

This talk focuses on the performance of Bayes estimators, 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 space and rather large sample sizes.

(Pizza, coffee & tea will be provided at 11:20am)