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Bayesian isotonic density regression.

Publication ,  Journal Article
Wang, L; Dunson, DB
Published in: Biometrika
September 2011

Density regression models allow the conditional distribution of the response given predictors to change flexibly over the predictor space. Such models are much more flexible than nonparametric mean regression models with nonparametric residual distributions, and are well supported in many applications. A rich variety of Bayesian methods have been proposed for density regression, but it is not clear whether such priors have full support so that any true data-generating model can be accurately approximated. This article develops a new class of density regression models that incorporate stochastic-ordering constraints which are natural when a response tends to increase or decrease monotonely with a predictor. Theory is developed showing large support. Methods are developed for hypothesis testing, with posterior computation relying on a simple Gibbs sampler. Frequentist properties are illustrated in a simulation study, and an epidemiology application is considered.

Duke Scholars

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Published In

Biometrika

DOI

EISSN

1464-3510

ISSN

0006-3444

Publication Date

September 2011

Volume

98

Issue

3

Start / End Page

537 / 551

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1403 Econometrics
  • 0104 Statistics
  • 0103 Numerical and Computational Mathematics
 

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Wang, L., & Dunson, D. B. (2011). Bayesian isotonic density regression. Biometrika, 98(3), 537–551. https://doi.org/10.1093/biomet/asr025
Wang, Lianming, and David B. Dunson. “Bayesian isotonic density regression.Biometrika 98, no. 3 (September 2011): 537–51. https://doi.org/10.1093/biomet/asr025.
Wang L, Dunson DB. Bayesian isotonic density regression. Biometrika. 2011 Sep;98(3):537–51.
Wang, Lianming, and David B. Dunson. “Bayesian isotonic density regression.Biometrika, vol. 98, no. 3, Sept. 2011, pp. 537–51. Epmc, doi:10.1093/biomet/asr025.
Wang L, Dunson DB. Bayesian isotonic density regression. Biometrika. 2011 Sep;98(3):537–551.
Journal cover image

Published In

Biometrika

DOI

EISSN

1464-3510

ISSN

0006-3444

Publication Date

September 2011

Volume

98

Issue

3

Start / End Page

537 / 551

Related Subject Headings

  • Statistics & Probability
  • 4905 Statistics
  • 3802 Econometrics
  • 1403 Econometrics
  • 0104 Statistics
  • 0103 Numerical and Computational Mathematics