A Maximum Likelihood Methodology for Clusterwise Linear Regression

Journal of Classification 5:249-282

34 Pages Posted: 30 May 2016

See all articles by Wayne S. DeSarbo

Wayne S. DeSarbo

Pennsylvania State University

William Cron

Southern Methodist University (SMU)

Date Written: 1988

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.

Keywords: Cluster analysis, Multiple regression, Maximum likelihood estimation, E-M algorithm, Marketing trade shows

Suggested Citation

DeSarbo, Wayne S. and Cron, William, A Maximum Likelihood Methodology for Clusterwise Linear Regression (1988). Journal of Classification 5:249-282, Available at SSRN: https://ssrn.com/abstract=2785822

Wayne S. DeSarbo (Contact Author)

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

William Cron

Southern Methodist University (SMU) ( email )

6212 Bishop Blvd.
Dallas, TX 75275
United States

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