THE UNIVERSITY OF BRITISH COLUMBIA DEPARTMENT OF STATISTICS TECHNICAL REPORT # 254 Grouping Priors and the Bayesian Elastic Net
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1 In the literature surrounding Bayesian penalized regression, the two primary choices of prior distribution on the regression coefficients are zero-mean Gaussian and Laplace. While both have been compared numerically and theoretically, there remains little guidance on which to use in real-life situations. We propose two viable solutions to this problem in the form of prior distributions which combine and compromise between Laplace and Gaussian priors, respectively. Through cross-validation the prior which optimizes prediction performance is automatically selected. We then demonstrate the improved performance of these new prior distributions relative to Laplace and Gaussian priors in both a simulated and experimental environment. Revivification of work presented at the CMS-MITACS Joint Conference, May 31 to June 3, 2007 ([2]). Since this time, considerable effort has been made on these and related models (i.e. [7], [3], and [6]). Our goal in producing this technical report is simply to make more readily available (in comparison to the poster format of the original) our initial contribution ([2]). 1
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تاریخ انتشار 2010