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A BAYESIAN ANALYSIS OF REGRESSION MODELS WITH CONTINUOUS ERRORS WITH APPLICATION TO LONGITUDINAL STUDIES
Author(s) -
BROEMELING LYLE D.,
COOK PEYTON
Publication year - 1997
Publication title -
statistics in medicine
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.996
H-Index - 183
eISSN - 1097-0258
pISSN - 0277-6715
DOI - 10.1002/(sici)1097-0258(19970228)16:4<321::aid-sim418>3.0.co;2-1
Subject(s) - bayesian probability , statistics , bayesian linear regression , autoregressive model , regression analysis , computer science , posterior probability , regression , econometrics , contrast (vision) , bayesian inference , bayesian average , mathematics , artificial intelligence
We employ a regression model with errors that follow a continuous autoregressive process to analyse longitudinal studies. In this way, unequally spaced observations do not present a problem in the analysis. We employ a Bayesian approach, where our inferences are based on a direct resampling process that generates values from the posterior distribution of the parameters of the model. We illustrate these Bayesian inferences with an analysis of a longitudinal study that involves the regression of foetal head circumference on menstrual age. Using these same data, we contrast the Bayesian approach with a maximum likelihood technique. © 1997 by John Wiley & Sons, Ltd.