Dr. Russell Kirby and colleagues publish paper on Bayesian semiparametric model

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Russell Kirby, PhD, MS, and colleagues published “Bayesian semiparametric model with spatially-temporally varying coefficients selection” in Statistics in Medicine.  Dr. Kirby is a professor and the Marrell Endowed Chair in the USF College of Public Health, Department of Community and Family Health. He directs the USF Birth Defects Surveillance Program and advises the Maternal and Child Health Student Organization.  In April, graduate students in public health presented him with the 2013 Outstanding Professor Award.

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Stat Med. 2013 Sep 20;32(21):3670-85. doi: 10.1002/sim.5789. Epub 2013 Mar 25.

Bayesian semiparametric model with spatially-temporally varying coefficients selection.

Cai B, Lawson AB, Hossain MM, Choi J, Kirby RS, Liu J.

Source

Department of Epidemiology and Biostatistics, University of South Carolina, Columbia, SC, U.S.A.

Abstract

In spatiotemporal analysis, the effect of a covariate on the outcome usually varies across areas and time. The spatial configuration of the areas may potentially depend on not only the structured random intercept but also spatially varying coefficients of covariates. In addition, the normality assumption of the distribution of spatially varying coefficients could lead to potential biases of estimations. In this article, we proposed a Bayesian semiparametric space-time model where the spatially-temporally varying coefficient is decomposed as fixed, spatially varying, and temporally varying coefficients. We nonparametrically modeled the spatially varying coefficients of space-time covariates by using the area-specific Dirichlet process prior with weights transformed via a generalized transformation. We modeled the temporally varying coefficients of covariates through the dynamic model. We also took into account the uncertainty of inclusion of the spatially-temporally varying coefficients by variable selection procedure through determining the probabilities of different effects for each covariate. The proposed semiparametric approach shows its improvement compared with the Bayesian spatial-temporal models with normality assumption on spatial random effects and the Bayesian model with the Dirichlet process prior on the random intercept. We presented a simulation example to evaluate the performance of the proposed approach with the competing models. We used an application to low birth weight data in South Carolina as an illustration. Copyright © 2013 John Wiley & Sons, Ltd.

Copyright © 2013 John Wiley & Sons, Ltd.

KEYWORDS:

Bayesian space-time models, area-specific Dirichlet process, spatially-temporally varying coefficients, variable selection

PMID:

23526312

[PubMed – in process]

PMCID:

PMC3744634

[Available on 2014/9/20]

 

 

 

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