Edward Greenberg

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This paper is concerned with the problems of posterior simulation and model choice for Poisson panel data models with multiple random effects. Efficient algorithms based on Markov chain Monte Carlo methods for sampling the posterior distribution are developed. A new parameterization of the random effects and fixed effects is proposed and compared with a(More)
In this paper we consider a nonparametric regression model in which the conditional variance function is assumed to vary smoothly with the predictor. We offer an easily implemented and fully Bayesian approach that involves the Markov chain Monte Carlo sampling of standard distributions. This method is based on a technique utilized by Kim, Shephard, and Chib(More)
The purpose of this article is to report the results of a comparison between the educational climate of a baccalaureate nursing program and that of a Nursing Workforce Diversity Grant. Fifteen Hispanic/Latino and American Indian upper-division baccalaureate nursing students completed 37 questionnaires. A 76-item questionnaire was designed to measure the(More)
The goal of this paper is to develop a flexible Bayesian analysis of regression models for continuous and categorical outcomes. In the models we study, covariate (or regression) effects are modeled additively by cubic splines, and the error distribution (that of the latent outcomes in the case of categorical data) is modeled as a Dirichlet process mixture.(More)
INTRODUCTION A growing body of evidence demonstrates the health benefits of muscular strength training. Physical activity recommendations encourage all adults to participate regularly in muscle strengthening activities. The purpose of this study was to examine the prevalence of meeting the US Department of Health and Human Services (DHHS) muscular(More)