Minimum distance estimation in a finite mixture regression model

Qingguo Tang and Rohana J. Karunamuni

Journal of Multivariate Analysis, 2013, vol. 120, issue C, 185-204

Abstract: Finite mixture models provide a mathematical basis for the statistical modeling of a wide variety of random situations, and their importance for the statistical analysis of data is well documented. This article focuses on a finite mixture regression model and develops an estimator of the parameters in the model using a minimum-distance technique. In general, minimum-distance estimators are consistent and asymptotically normal when the data come from a member of the model family. Furthermore, it has been observed that they are “automatically robust” with respect to the stability of the quantity being estimated. In this paper, we employ the Hellinger distance approach introduced by Beran (1977) [5] and construct a minimum Hellinger distance estimator for a finite mixture regression model. We study the asymptotic properties such as consistency and the asymptotic normality of the proposed estimator. The small-sample and robustness properties of the proposed estimator are also examined using a Monte Carlo study, and a computational algorithm is presented.

Keywords: Finite mixture regression model; Minimum Hellinger distance estimators; Asymptotic normality; Robust estimators (search for similar items in EconPapers)
Date: 2013
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0047259X13000948
Full text for ScienceDirect subscribers only

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:eee:jmvana:v:120:y:2013:i:c:p:185-204

Ordering information: This journal article can be ordered from
http://www.elsevier.com/wps/find/supportfaq.cws_home/regional
https://shop.elsevie ... _01_ooc_1&version=01

DOI: 10.1016/j.jmva.2013.05.008

Access Statistics for this article

Journal of Multivariate Analysis is currently edited by de Leeuw, J.

More articles in Journal of Multivariate Analysis from Elsevier
Bibliographic data for series maintained by Catherine Liu ().