Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

Likelihood adaptively modified penalties

Likelihood adaptively modified penalties A new family of penalty functions, ie, adaptive to likelihood, is introduced for model selection in general regression models. It arises naturally through assuming certain types of prior distribution on the regression parameters. To study the stability properties of the penalized maximum‐likelihood estimator, 2 types of asymptotic stability are defined. Theoretical properties, including the parameter estimation consistency, model selection consistency, and asymptotic stability, are established under suitable regularity conditions. An efficient coordinate‐descent algorithm is proposed. Simulation results and real data analysis show that the proposed approach has competitive performance in comparison with the existing methods. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Applied Stochastic Models in Business and Industry Wiley

Loading next page...
 
/lp/wiley/likelihood-adaptively-modified-penalties-lbFc2qgEUN

References (55)

Publisher
Wiley
Copyright
"© 2019 John Wiley & Sons, Ltd."
ISSN
1524-1904
eISSN
1526-4025
DOI
10.1002/asmb.2322
Publisher site
See Article on Publisher Site

Abstract

A new family of penalty functions, ie, adaptive to likelihood, is introduced for model selection in general regression models. It arises naturally through assuming certain types of prior distribution on the regression parameters. To study the stability properties of the penalized maximum‐likelihood estimator, 2 types of asymptotic stability are defined. Theoretical properties, including the parameter estimation consistency, model selection consistency, and asymptotic stability, are established under suitable regularity conditions. An efficient coordinate‐descent algorithm is proposed. Simulation results and real data analysis show that the proposed approach has competitive performance in comparison with the existing methods.

Journal

Applied Stochastic Models in Business and IndustryWiley

Published: Mar 1, 2019

Keywords: ; ; ; ; ; ;

There are no references for this article.