Palestra: Robust Mean Aggregation with the Bayesian Median of Means

Palestrante: Paulo Orenstein (Stanford University)

Dando continuidade ao Ciclo de Palestras do Programa de Pós-Graduação em Estatística do IM-UFRJ, nesta 4a feira:

Data: 04 de setembro de 2019
Horário: 15:30
Local: Laboratório de Sistemas Estocásticos (LSE), sala I-044, - CT - UFRJ.

Resumo: The sample mean is often used to aggregate different unbiased estimates of a real parameter, producing a final estimate that is unbiased but possibly high-variance. As an alternative, the Bayesian median of means is proposed, an aggregation rule that roughly interpolates between the sample mean and median, resulting in estimates with much smaller variance at the expense of bias. While the procedure is non-parametric, its squared bias is asymptotically negligible relative to the variance, similar to maximum likelihood estimators. The Bayesian median of means is consistent, and concentration bounds for the estimator’s bias and L1 error are derived, as well as a fast non-randomized approximating algorithm. The performances of both the exact and the approximate procedures match that of the sample mean in low-variance settings, and exhibit much better results in high-variance scenarios. The empirical performances are examined in real and simulated data, and in applications such as importance sampling, cross-validation and bagging.

Acompanhem a atualização da programação do ciclo de palestras no site www.dme.ufrj.br opção Atividades subopção Ciclo de Palestras.