Although the applications are from a variety of areas, the emphasis in this volume is on econometrics. The first two volumes have proved to be valuable references for statistical instructors and data analysts. In general, I found many of the papers collected in this book interesting One might consider using this book as supplementary material in graduate-level seminar courses on Bayesian data analysis.http://tax-marusa.com/order/ligokitin/comment-espionner-whatsapp-gratuit.php
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Softcover reprint of the original 1st ed. Seller Inventory AAV Seller Inventory LIE Publisher: Springer , Hierarchical Bayes models provide a natural way of incorporating covariate information into the inferential process through the elaboration of regression equations for one or more of the model parameters, with errors that are often assumed to be i. Unfortunately, building adequate regression models is a complicated art form that requires the practitioner to make numerous decisions along the way.
Assessing the validity of the modeling decisions is often difficult. In this article I develop a simple and effective device for ascertaining the quality of the modeling choices and detecting lack-of-fit.
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I specify an artificial autoregressive structure AAR in the probability model for the errors that incorporates the i. Lack-of-fit can be detected by examining the posterior distribution of AAR parameters. In general, posterior distributions that assign considerable mass to a region of the AAR parameter space away from zero provide evidence that apparent dependencies in the errors are compensating for misspecifications of some other aspects typically conditional means of the model.
I illustrate the methodology through several examples including its application to the analysis of data on brain and body weights of mammalian species and response time data.
Case Studies in Bayesian Statistics
Source Bayesian Anal. Zentralblatt MATH identifier Peruggia, Mario. Bayesian model diagnostics based on artificial autoregressive errors. Bayesian Anal.