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2016 Transformations and Bayesian density estimation
Andrew Bean, Xinyi Xu, Steven MacEachern
Electron. J. Statist. 10(2): 3355-3373 (2016). DOI: 10.1214/16-EJS1158

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 estimation of the density on the new scale. The effectiveness of the method is demonstrated under a variety of simulated scenarios, and in an application to body mass index (BMI) observations from a large survey of Ohio adults.

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Andrew Bean. Xinyi Xu. Steven MacEachern. "Transformations and Bayesian density estimation." Electron. J. Statist. 10 (2) 3355 - 3373, 2016. https://doi.org/10.1214/16-EJS1158

Information

Received: 1 January 2016; Published: 2016
First available in Project Euclid: 16 November 2016

zbMATH: 1358.62038
MathSciNet: MR3572853
Digital Object Identifier: 10.1214/16-EJS1158

Rights: Copyright © 2016 The Institute of Mathematical Statistics and the Bernoulli Society

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Vol.10 • No. 2 • 2016
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