Statistics Department

Topic:

A Bayesian Nonparametric Multiple Testing Procedure for Comparing Several Treatments Against a Control

Abstract:

We propose a Bayesian nonparametric strategy to test for differences between a control group and several treatment regimes. Most of the existing tests for this type of comparison are based on the differences between location parameters. In contrast, our approach identifies differences across the entire distribution, avoids strong modeling assumptions over the distributions for each treatment, and accounts for multiple testing through the prior distribution on the space of hypotheses. The proposed method is compared to other commonly used hypothesis testing procedures under diverse simulated scenarios. A real applications is also analyzed with the proposed methodology.

For more information about Daniel Taylor-Rodriguez, click here.