A new model selection procedure for finite mixture regression models
Conglian Yu and
Xiyang Wang
Communications in Statistics - Theory and Methods, 2020, vol. 49, issue 18, 4347-4366
Abstract:
In this article, we propose a new penalized-likelihood method to conduct model selection for finite mixture of regression models. The penalties are imposed on mixing proportions and regression coefficients, and hence order selection of the mixture and the variable selection in each component can be simultaneously conducted. The consistency of order selection and the consistency of variable selection are investigated. A modified EM algorithm is proposed to maximize the penalized log-likelihood function. Numerical simulations are conducted to demonstrate the finite sample performance of the estimation procedure. The proposed methodology is further illustrated via real data analysis.
Date: 2020
References: Add references at CitEc
Citations:
Downloads: (external link)
http://hdl.handle.net/10.1080/03610926.2019.1601222 (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:49:y:2020:i:18:p:4347-4366
Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/lsta20
DOI: 10.1080/03610926.2019.1601222
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 ().