A SAS Interface for Bayesian Analysis With WinBUGS

Bayesian methods are becoming very popular despite some practical difficulties in implementation. To assist in the practical application of Bayesian methods, we show how to implement Bayesian analysis with WinBUGS as part of a standard set of SAS routines. This implementation procedure is first illustrated by fitting a multiple regression model and then a linear growth curve model. A third example is also provided to demonstrate how to iteratively run WinBUGS inside SAS for Monte Carlo simulation studies. The SAS codes used in this study are easily extended to accommodate many other models with only slight modification. This interface can be of practical benefit in many aspects of Bayesian methods because it allows the SAS users to benefit from the implementation of Bayesian estimation and it also allows the WinBUGS user to benefit from the data processing routines available in SAS.

[1]  Hua-Hua Chang,et al.  The asymptotic posterior normality of the latent trait for polytomous IRT models , 1996 .

[2]  D. Trafimow Hypothesis testing and theory evaluation at the boundaries: surprising insights from Bayes's theorem. , 2003, Psychological review.

[3]  John J. McArdle,et al.  A Simulation Study Comparison of Bayesian Estimation With Conventional Methods for Estimating Unknown Change Points , 2008 .

[4]  E. Demidenko,et al.  Mixed Models: Theory and Applications (Wiley Series in Probability and Statistics) , 2004 .

[5]  Alaattin Erkanli,et al.  Markov Chain Monte Carlo Approaches to Analysis of Genetic and Environmental Components of Human Developmental Change and G × E Interaction , 2003 .

[6]  William Meredith,et al.  Latent curve analysis , 1990 .

[7]  Sy-Miin Chow,et al.  Examining Interindividual Differences in Cyclicity of Pleasant and Unpleasant Affects Using Spectral Analysis and Item Response Modeling , 2005 .

[8]  Ryan P. Bowles Item Response Models for Intratask Change to Examine theImpacts of Proactive Interference on the Aging of Working MemorySpan , 2006 .

[9]  Michael D. Lee,et al.  A Bayesian analysis of retention functions , 2004 .

[10]  Sik-Yum Lee A bayesian approach to confirmatory factor analysis , 1981 .

[11]  Wing Hung Wong,et al.  Bayesian Analysis in Applications of Hierarchical Models: Issues and Methods , 1996 .

[12]  I. J. Myung,et al.  Applying Occam’s razor in modeling cognition: A Bayesian approach , 1997 .

[13]  Mary Kathryn Cowles,et al.  Review of WinBUGS 1.4 , 2004 .

[14]  J. Ware,et al.  Random-effects models for longitudinal data. , 1982, Biometrics.

[15]  Jie W Weiss,et al.  Bayesian Statistical Inference for Psychological Research , 2008 .

[16]  D. Bartholomew Posterior analysis of the factor model , 1981 .

[17]  Ellen L. Hamaker,et al.  Comparisons of Four Methods for Estimating a Dynamic Factor Model , 2008 .

[18]  M. Lee,et al.  Bayesian statistical inference in psychology: comment on Trafimow (2003). , 2005, Psychological review.

[19]  D. Dey,et al.  To Bayes or Not to Bayes, From Whether to When: Applications of Bayesian Methodology to Modeling , 2004 .

[20]  Zhiyong Zhang,et al.  Bayesian Estimation of Categorical Dynamic Factor Models , 2007 .

[21]  John J. McArdle,et al.  Growth Curve Analysis in Contemporary Psychological Research , 2003 .

[22]  John R. Nesselroade,et al.  Bayesian analysis of longitudinal data using growth curve models , 2007 .

[23]  R. Scheines,et al.  Bayesian estimation and testing of structural equation models , 1999 .

[24]  D. Trafimow The Ubiquitous Laplacian Assumption: Reply to Lee and Wagenmakers (2005). , 2005 .

[25]  Bradley P. Carlin,et al.  Bayesian measures of model complexity and fit , 2002 .

[26]  Markov Chain Monte Carlo approaches to analysis of genetic and environmental components of human developmental change and G x E interaction. , 2003, Behavior genetics.

[27]  J. Fox,et al.  Bayesian estimation of a multilevel IRT model using gibbs sampling , 2001 .