A Maximum Likelihood Methodology for Clusterwise Linear Regression
Journal of Classification 5:249-282
34 Pages Posted: 30 May 2016
Date Written: 1988
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
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