How collinearity affects mixture regression results - Kölner UniversitätsPublikationsServer

Becker, Jan-Michael ORCID: 0000-0003-3013-3739, Ringle, Christian M. ORCID: 0000-0002-7027-8804, Sarstedt, Marko ORCID: 0000-0002-5424-4268 and Voelckner, Franziska (2015). How collinearity affects mixture regression results. Mark. Lett., 26 (4). S. 643 - 660. DORDRECHT: SPRINGER. ISSN 1573-059X

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DOI:10.1007/s11002-014-9299-9

Abstract

Mixture regression models are an important method for uncovering unobserved heterogeneity. A fundamental challenge in their application relates to the identification of the appropriate number of segments to retain from the data. Prior research has provided several simulation studies that compare the performance of different segment retention criteria. Although collinearity between the predictor variables is a common phenomenon in regression models, its effect on the performance of these criteria has not been analyzed thus far. We address this gap in research by examining the performance of segment retention criteria in mixture regression models characterized by systematically increased collinearity levels. The results have fundamental implications and provide guidance for using mixture regression models in empirical (marketing) studies.

Item Type:

Journal Article

Creators:

Creators	Email	ORCID	ORCID Put Code
Becker, Jan-Michael	UNSPECIFIED	orcid.org/0000-0003-3013-3739	UNSPECIFIED
Ringle, Christian M.	UNSPECIFIED	orcid.org/0000-0002-7027-8804	UNSPECIFIED
Sarstedt, Marko	UNSPECIFIED	orcid.org/0000-0002-5424-4268	UNSPECIFIED
Voelckner, Franziska	UNSPECIFIED	UNSPECIFIED	UNSPECIFIED