Selection of tuning parameters in bridge regression models via Bayesian information criterion

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)

Abstract

We consider bridge regression models, which can produce a sparse or non-sparse model by controlling a tuning parameter in the penalty term. A crucial part of a model building strategy is the selection of the values for adjusted parameters, such as regularization and tuning parameters. Indeed, this can be viewed as a problem in selecting and evaluating the model. We propose a Bayesian selection criterion for evaluating bridge regression models. This criterion enables us to objectively select the values of the adjusted parameters. We investigate the effectiveness of our proposed modeling strategy with some numerical examples.

Original languageEnglish
Pages (from-to)1207-1223
Number of pages17
JournalStatistical Papers
Volume55
Issue number4
DOIs
Publication statusPublished - Oct 5 2014
Externally publishedYes

All Science Journal Classification (ASJC) codes

  • Statistics and Probability
  • Statistics, Probability and Uncertainty

Fingerprint

Dive into the research topics of 'Selection of tuning parameters in bridge regression models via Bayesian information criterion'. Together they form a unique fingerprint.

Cite this