Volume 10, Issue 2 (November 2011)                   JIRSS 2011, 10(2): 125-140 | Back to browse issues page

XML Persian Abstract Print

Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:

Zhang C, Zhang Z, Chai Y. Penalized Bregman Divergence Estimation via Coordinate Descent. JIRSS. 2011; 10 (2) :125-140
URL: http://jirss.irstat.ir/article-1-160-en.html
Abstract:   (11961 Views)
Variable selection via penalized estimation is appealing for dimension reduction. For penalized linear regression, Efron, et al. (2004) introduced the LARS algorithm. Recently, the coordinate descent (CD) algorithm was developed by Friedman, et al. (2007) for penalized linear regression and penalized logistic regression and was shown to gain computational superiority. This paper explores the CD algorithm to penalized Bregman divergence (BD) estimation for a broader class of models, including not only the generalized linear model, which has been well studied in the literature on penalization, but also the quasi-likelihood model, which has been less developed. Simulation study and real data application illustrate the performances of the CD and LARS algorithms in regression estimation, variable selection and classification procedure when the number of explanatory variables is large in comparison to the sample size.
Full-Text [PDF 138 kb]   (3541 Downloads)    

Received: 2011/11/7 | Accepted: 2015/09/12 | Published: 2011/11/15

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

© 2015 All Rights Reserved | Journal of The Iranian Statistical Society