Bayesian marginal equivalence of elliptical regression models
The use of proper prior densities in regression models with multivariate non-Normal elliptical error distributions is examined when the scale matrix is known up to a precision factor T, treated as a nuisance parameter. Marginally equivalent models preserve the convenient predictive and posterior results on the parameter of interest B obtained in the reference case of the Normal model and its conditionally natural conjugate gamma prior. Prior densities inducing this property are derived for two special cases of non-Normal elliptical densities representing very different patterns of tail behavior. In a linear framework, so-called semi-conjugate prior structures are defined as leading to marginal equivalence to a Normal data density with a fully natural conjugate prior.
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- Osiewalski, J., 1989.
"A Note On Bayesian Inference In A Regression Model With Elliptical Errors,"
CORE Discussion Papers
1989040, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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- OSIEWALSKI, Jacek, "undated". "A note on Bayesian inference in a regression model with elliptical errors," CORE Discussion Papers RP 926, Université catholique de Louvain, Center for Operations Research and Econometrics (CORE).
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