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Assessing Heterogeneity in Discrete Choice Models Using a Dirichlet Process Prior

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Authors
Kim, Jin Gyo; Menzefricke, Ulrich; M. Feinberg, Fred
Issue Date
2004
Publisher
The Berkeley Electronic Press
Citation
Review of Marketing Science, 2, 1-39
Keywords
Choice ModelsHeterogeneityDirichlet ProcessBeyesian MethodsMarkov chain Monte Carlo
Abstract
The finite normal mixture model has emerged as a dominant methodology for assessing heterogeneity in choice models. Although it extends the classic mixture models by allowing within component variablility, it requires that a relatively large number of models be separately estimated and fairly difficult test procedures to determine the correct number of mixing components. We present a very general formulation, based on Dirichlet Process Piror, which yields the number and composition of mixing components a posteriori, obviating the need for post hoc test procedures and is capable of approximating any target heterogeneity distribution. Adapting Stephens (2000) algorithm allows the determination of substantively different clusters, as well as a way to sidestep problems arising from label-switching and overlapping mixtures. These methods are illustrated both on simulated data and A.C. Nielsen scanner panel data for liquid detergents. We find that the large number of mixing components required to adequately represent the heterogeneity distribution can be reduced in practice to a far smaller number of segments of managerial relevance.
ISSN
1546-5616
Language
English
URI
http://webuser.bus.umich.edu/feinf/research/Published/Kim,_Menzefricke,_Feinberg_(2004)_-_Dirichlet_Heterogeneity_ROMS.pdf

https://hdl.handle.net/10371/67955
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College of Business Administration/Business School (경영대학/대학원)Dept. of Business Administration (경영학과)Journal Papers (저널논문_경영학과)
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