Sample- and segment-size specific Model Selection in Mixture Regression Analysis

Marko Sarstedt

Discussion Papers in Business Administration from University of Munich, Munich School of Management

Abstract: As mixture regression models increasingly receive attention from both theory and practice, the question of selecting the correct number of segments gains urgency. A misspecification can lead to an under- or oversegmentation, thus resulting in flawed management decisions on customer targeting or product positioning. This paper presents the results of an extensive simulation study that examines the performance of commonly used information criteria in a mixture regression context with normal data. Unlike with previous studies, the performance is evaluated at a broad range of sample/segment size combinations being the most critical factors for the effectiveness of the criteria from both a theoretical and practical point of view. In order to assess the absolute performance of each criterion with respect to chance, the performance is reviewed against so called chance criteria, derived from discriminant analysis. The results induce recommendations on criterion selection when a certain sample size is given and help to judge what sample size is needed in order to guarantee an accurate decision based on a certain criterion respectively.

Keywords: Mixture Regression; Model Selection; Information Criteria (search for similar items in EconPapers)
JEL-codes: C30 M31 (search for similar items in EconPapers)
Date: 2006
New Economics Papers: this item is included in nep-ecm
References: View complete reference list from CitEc
Citations: View citations in EconPapers (2)

Downloads: (external link)
https://epub.ub.uni-muenchen.de/1252/1/2006_08_LMU_sarstedt.pdf (application/pdf)

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:lmu:msmdpa:1252

Access Statistics for this paper

More papers in Discussion Papers in Business Administration from University of Munich, Munich School of Management Ludwigstr. 28,80539 Munich, Germany. Contact information at EDIRC.
Bibliographic data for series maintained by Mareike Seifried ( this e-mail address is bad, please contact ).