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Nonparametric Bayes local partition models for random effects

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  • David B. Dunson

Abstract

This paper focuses on the problem of choosing a prior for an unknown random effects distribution within a Bayesian hierarchical model. The goal is to obtain a sparse representation by allowing a combination of global and local borrowing of information. A local partition process prior is proposed, which induces dependent local clustering. Subjects can be clustered together for a subset of their parameters, and one learns about similarities between subjects increasingly as parameters are added. Some basic properties are described, including simple two-parameter expressions for marginal and conditional clustering probabilities. A slice sampler is developed which bypasses the need to approximate the countably infinite random measure in performing posterior computation. The methods are illustrated using simulation examples, and an application to hormone trajectory data. Copyright 2009, Oxford University Press.

Suggested Citation

  • David B. Dunson, 2009. "Nonparametric Bayes local partition models for random effects," Biometrika, Biometrika Trust, vol. 96(2), pages 249-262.
    • Handle: RePEc:oup:biomet:v:96:y:2009:i:2:p:249-262
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    File URL: http://hdl.handle.net/10.1093/biomet/asp021
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    Cited by:

    1. Laura Liu, 2018. "Density Forecasts in Panel Data Models : A Semiparametric Bayesian Perspective," Finance and Economics Discussion Series 2018-036, Board of Governors of the Federal Reserve System (U.S.).
    2. Silvia Montagna & Surya T. Tokdar & Brian Neelon & David B. Dunson, 2012. "Bayesian Latent Factor Regression for Functional and Longitudinal Data," Biometrics, The International Biometric Society, vol. 68(4), pages 1064-1073, December.
    3. Subharup Guha & Rex Jung & David Dunson, 2022. "Predicting phenotypes from brain connection structure," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 71(3), pages 639-668, June.
    4. Laura Liu, 2017. "Density Forecasts in Panel Models: A semiparametric Bayesian Perspective," PIER Working Paper Archive 17-006, Penn Institute for Economic Research, Department of Economics, University of Pennsylvania, revised 28 Apr 2017.
    5. Eric Coker & Robert Gunier & Asa Bradman & Kim Harley & Katherine Kogut & John Molitor & Brenda Eskenazi, 2017. "Association between Pesticide Profiles Used on Agricultural Fields near Maternal Residences during Pregnancy and IQ at Age 7 Years," IJERPH, MDPI, vol. 14(5), pages 1-20, May.
    6. Liverani, Silvia & Hastie, David I. & Azizi, Lamiae & Papathomas, Michail & Richardson, Sylvia, 2015. "PReMiuM: An R Package for Profile Regression Mixture Models Using Dirichlet Processes," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 64(i07).

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