A random model approach for the LASSO

Authors

Scott D. Foster, University of Adelaide
Arunas P. Verbyla, University of AdelaideFollow
Wayne S. Pitchford, University of Adelaide

RIS ID

104981

Publication Details

Foster, S. D., Verbyla, A. P. & Pitchford, W. S.. (2008). A random model approach for the LASSO. Computational Statistics, 23 (2), 217-233.

Abstract

The least absolute selection and shrinkage operator (LASSO) is a method of estimation for linear models similar to ridge regression. It shrinks the effect estimates, potentially shrinking some to be identically zero. The amount of shrinkage is governed by a single parameter. Using a random model formulation of the LASSO, this parameter can be specified as the ratio of dispersion parameters. These parameters are estimated using an approximation to the marginal likelihood of the observed data. The observed score equations from the approximation are biased and hence are adjusted by subtracting an empirical estimate of the expected value. After estimation, the model effects can be tested (via simulation) as the distribution of the observed data given that all model effects are zero is known. Two related simulation studies are presented that show that dispersion parameter estimation results in effect estimates that are competitive with other estimation methods (including other LASSO methods).