A maximum likelihood methodology for clusterwise linear regression

Wayne S. DeSarbo, William L. Cron

Research output: Contribution to journalArticle

269 Scopus citations

Abstract

This paper presents a conditional mixture, maximum likelihood methodology for performing clusterwise linear regression. This new methodology simultaneously estimates separate regression functions and membership in K clusters or groups. A review of related procedures is discussed with an associated critique. The conditional mixture, maximum likelihood methodology is introduced together with the E-M algorithm utilized for parameter estimation. A Monte Carlo analysis is performed via a fractional factorial design to examine the performance of the procedure. Next, a marketing application is presented concerning the evaluations of trade show performance by senior marketing executives. Finally, other potential applications and directions for future research are identified.

Original languageEnglish (US)
Pages (from-to)249-282
Number of pages34
JournalJournal of Classification
Volume5
Issue number2
DOIs
StatePublished - Sep 1 1988

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All Science Journal Classification (ASJC) codes

  • Mathematics (miscellaneous)
  • Psychology (miscellaneous)
  • Statistics, Probability and Uncertainty
  • Library and Information Sciences

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