Testing Sparsity-Inducing Penalties

TL;DR

This paper develops a statistical test to determine if a Laplace prior is suitable for penalized regression, and introduces an adaptive method to select more appropriate priors, improving estimation accuracy.

Contribution

It proposes a testing procedure for Laplace prior suitability and an adaptive approach to select better priors, enhancing penalized regression estimation.

Findings

Test achieves desired significance level and detects violations effectively.

Adaptive procedure improves estimation under various prior distributions.

Method performs well with large sample sizes and many coefficients.

Abstract

Many penalized maximum likelihood estimators correspond to posterior mode estimators under specific prior distributions. Appropriateness of a particular class of penalty functions can therefore be interpreted as the appropriateness of a prior for the parameters. For example, the appropriateness of a lasso penalty for regression coefficients depends on the extent to which the empirical distribution of the regression coefficients resembles a Laplace distribution. We give a testing procedure of whether or not a Laplace prior is appropriate and accordingly, whether or not using a lasso penalized estimate is appropriate. This testing procedure is designed to have power against exponential power priors which correspond to $\ell_q$ penalties. Via simulations, we show that this testing procedure achieves the desired level and has enough power to detect violations of the Laplace assumption when the numbers of observations and unknown regression coefficients are large. We then introduce an adaptive procedure that chooses a more appropriate prior and corresponding penalty from the class of exponential power priors when the null hypothesis is rejected. We show that this can improve estimation of the regression coefficients both when they are drawn from an exponential power distribution and when they are drawn from a spike-and-slab distribution.