Transformation and Bayesian Density Estimation

• Activity Number: 263
• Type: Contributed
• Date/Time: Monday, August 10, 2015 : 2:00 PM to 3:50 PM
• Sponsor: Section on Bayesian Statistical Science
• Author(s): Andrew Bean* and Steven MacEachern and Xinyi Xu
• Companies: and The Ohio State University and The Ohio State University
• Keywords: density estimation ; transformation ; dirichlet process

Abstract

Dirichlet-process mixture models, favored for their large support and for the relative ease of their implementation, are popular choices for Bayesian density estimation. However, despite the models' flexibility, the performance of density estimates suffers in certain situations, in particular when the true distribution is skewed or heavy tailed. We detail a method that improves performance in a variety of settings by initially transforming the sample, choosing the transformation to facilitate the subsequent density estimation procedure. The effectiveness of the method is demonstrated under a variety of simulated scenarios, and in an application to body mass index (BMI) observations from Ohio counties.