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Comparing Bayes Model Averaging And Stacking When Model Approximation Error Cannot Be Ignored, Bertrand S. Clarke
Comparing Bayes Model Averaging And Stacking When Model Approximation Error Cannot Be Ignored, Bertrand S. Clarke
Department of Statistics: Faculty Publications
We compare Bayes Model Averaging, BMA, to a non-Bayes form of model averaging called stacking. In stacking, the weights are no longer posterior probabilities of models; they are obtained by a technique based on cross-validation. When the correct data generating model (DGM) is on the list of models under consideration BMA is never worse than stacking and often is demonstrably better, provided that the noise level is of order commensurate with the coefficients and explanatory variables. Here, however, we focus on the case that the correct DGM is not on the model list and may not be well approximated by …