How collinearity affects mixture regression results
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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.
KeywordsMarket segmentation Segment retention Mixture regression Collinearity
The authors would like to thank Jörg Henseler (Radboud University Nijmegen) and Edward E. Rigdon (Georgia State University) for their comments on earlier versions of the paper.
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