EconPapers    
Economics at your fingertips  
 

Robust variable selection for finite mixture regression models

Qingguo Tang and R. J. Karunamuni ()
Additional contact information
Qingguo Tang: Nanjing University of Science and Technology
R. J. Karunamuni: University of Alberta

Annals of the Institute of Statistical Mathematics, 2018, vol. 70, issue 3, 489-521

Abstract: Abstract Finite mixture regression (FMR) models are frequently used in statistical modeling, often with many covariates with low significance. Variable selection techniques can be employed to identify the covariates with little influence on the response. The problem of variable selection in FMR models is studied here. Penalized likelihood-based approaches are sensitive to data contamination, and their efficiency may be significantly reduced when the model is slightly misspecified. We propose a new robust variable selection procedure for FMR models. The proposed method is based on minimum-distance techniques, which seem to have some automatic robustness to model misspecification. We show that the proposed estimator has the variable selection consistency and oracle property. The finite-sample breakdown point of the estimator is established to demonstrate its robustness. We examine small-sample and robustness properties of the estimator using a Monte Carlo study. We also analyze a real data set.

Keywords: Finite mixture regression models; Variable selection; Minimum-distance methods (search for similar items in EconPapers)
Date: 2018
References: View references in EconPapers View complete reference list from CitEc
Citations: Track citations by RSS feed

Downloads: (external link)
http://link.springer.com/10.1007/s10463-017-0602-4 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

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:spr:aistmt:v:70:y:2018:i:3:d:10.1007_s10463-017-0602-4

Ordering information: This journal article can be ordered from
http://www.springer. ... cs/journal/10463/PS2

Access Statistics for this article

Annals of the Institute of Statistical Mathematics is currently edited by Tomoyuki Higuchi

More articles in Annals of the Institute of Statistical Mathematics from Springer, The Institute of Statistical Mathematics
Bibliographic data for series maintained by Sonal Shukla ().

 
Page updated 2019-05-21
Handle: RePEc:spr:aistmt:v:70:y:2018:i:3:d:10.1007_s10463-017-0602-4