Penalized Lq-likelihood estimators and variable selection in linear regression models

Hongchang Hu and Zhen Zeng

Communications in Statistics - Theory and Methods, 2022, vol. 51, issue 17, 5957-5970

Abstract: Consider a linear regression model yi=xiTβ+ei,i=1,2,…,n, where {ei} are independent identically distributed (iid) random variables with zero mean and known variance σ2. Based on the maximum Lq-likelihood estimator (MLqE) and the penalized likelihood estimator (PLE), we introduce a new parametric estimator which is called penalized Lq-likelihood estimator (PLqE). We investigate its Oracle properties and influence function. Simulation results support the validity of our approach. Furthermore, it is shown that the PLqE is robust, while the PLE is not.

Date: 2022
References: Add references at CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2020.1850794 (text/html)
Access to full text is restricted to subscribers.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:taf:lstaxx:v:51:y:2022:i:17:p:5957-5970

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20

DOI: 10.1080/03610926.2020.1850794

Access Statistics for this article

Communications in Statistics - Theory and Methods is currently edited by Debbie Iscoe

More articles in Communications in Statistics - Theory and Methods from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().