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Bayesian variable selection using the hyper‐g prior in WinBUGS

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The hyper‐g prior is a default choice for Bayesian variable selection in normal linear regression models. In this article we provide an overview of the Bayesian variable selection framework and explain in detail the specification for the hyper‐g prior setup. The practical implementation of the methods under consideration is demonstrated through the use of WinBUGS software explaining the correspondence between code and theoretical setup. An illustration of results is considered through a simulated data example. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory Statistical Models > Model Selection Statistical Models > Linear Models
Posterior inclusion probabilities for each method (covariate index is ordered by the size inclusion probabilities)
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Bayesian model averaged posterior distributions of coefficients (on the left) and posterior inclusion probabilities (on the right) for covariates X4, X5, X10, and X12
[ Normal View | Magnified View ]

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Statistical and Graphical Methods of Data Analysis > Bayesian Methods and Theory
Statistical Models > Model Selection
Statistical Models > Linear Models

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