Grouping Priors and the Bayesian Elastic Net

Details Grouping Priors and the Bayesian Elastic Net Grouping Priors and the Bayesian Elastic Net

2010-01-22

arxiv.org/abs/1001.4083v1

Statistics

Methodology

Scientific paper

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.

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