How collinearity affects mixture regression results

Title: How collinearity affects mixture regression results
Creator: Becker, Jan-Michael; Ringle, Christian M.; Sarstedt, Marko; Völckner, Franziska
Relation: Marketing Letters Vol. 26, Issue 4, p. 643-659
Publisher Link: http://dx.doi.org/10.1007/s11002-014-9299-9
Publisher: Wiley-Blackwell
Resource Type: journal article
Date: 2015
Description: 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.
Subject: market segmentation; segment retention; mixture regression; collinerarity
Identifier: http://hdl.handle.net/1959.13/1327697
Identifier: uon:25724
Identifier: ISSN:0923-0645
Language: eng
Reviewed

		Thumbnail	File	Description	Size	Format