Bayesian Computation for Parametric Models of Heteroscedasticity in the Linear Model (1994)
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BibTeX
@MISC{Boscardin94bayesiancomputation,
author = {W. John Boscardin and Andrew Gelman},
title = {Bayesian Computation for Parametric Models of Heteroscedasticity in the Linear Model},
year = {1994}
}
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Abstract
In the linear model with unknown variances, one can often model the heteroscedasticity as var(y i ) = oe 2 f(w i ; `); where f is a fixed function, w i are the "weights" for the problem and ` is an unknown parameter (f(w i ; `) = w \Gamma` i is a traditional choice). We show how to do a fully Bayesian computation in this simple linear setting and also for a hierarchical model. The full Bayesian computation has the advantage that we are able to average over our uncertainty in ` instead of using a point estimate. We carry out the computations for a problem involving forecasting U.S. Presidential elections, looking at different choices for f and the effects on both estimation and prediction.
