| Bayesian isotonic regression and trend analysis. | |
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MedLine Citation:
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PMID: 15180665 Owner: NLM Status: MEDLINE |
Abstract/OtherAbstract:
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In many applications, the mean of a response variable can be assumed to be a nondecreasing function of a continuous predictor, controlling for covariates. In such cases, interest often focuses on estimating the regression function, while also assessing evidence of an association. This article proposes a new framework for Bayesian isotonic regression and order-restricted inference. Approximating the regression function with a high-dimensional piecewise linear model, the nondecreasing constraint is incorporated through a prior distribution for the slopes consisting of a product mixture of point masses (accounting for flat regions) and truncated normal densities. To borrow information across the intervals and smooth the curve, the prior is formulated as a latent autoregressive normal process. This structure facilitates efficient posterior computation, since the full conditional distributions of the parameters have simple conjugate forms. Point and interval estimates of the regression function and posterior probabilities of an association for different regions of the predictor can be estimated from a single MCMC run. Generalizations to categorical outcomes and multiple predictors are described, and the approach is applied to an epidemiology application. |
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Authors:
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Brian Neelon; David B Dunson |
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Publication Detail:
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Type: Journal Article |
Journal Detail:
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Title: Biometrics Volume: 60 ISSN: 0006-341X ISO Abbreviation: Biometrics Publication Date: 2004 Jun |
Date Detail:
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Created Date: 2004-06-07 Completed Date: 2005-01-14 Revised Date: - |
Medline Journal Info:
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Nlm Unique ID: 0370625 Medline TA: Biometrics Country: United States |
Other Details:
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Languages: eng Pagination: 398-406 Citation Subset: IM |
Affiliation:
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Department of Biostatistics, CB no. 7420, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina 27599, USA. |
Export Citation:
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APA/MLA Format Download EndNote Download BibTex |
| MeSH Terms | |
Descriptor/Qualifier:
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Bayes Theorem* Biometry* Dichlorodiphenyl Dichloroethylene / toxicity Epidemiologic Methods Female Humans Infant, Newborn Insecticides / toxicity Linear Models Markov Chains Monte Carlo Method Pregnancy Premature Birth / chemically induced Regression Analysis* |
| Chemical | |
Reg. No./Substance:
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0/Insecticides; 72-55-9/Dichlorodiphenyl Dichloroethylene |
From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine
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