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A maximum likelihood methodology for clusterwise linear regression. (English) Zbl 0692.62052

Summary: 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.

MSC:

62H30 Classification and discrimination; cluster analysis (statistical aspects)
62P20 Applications of statistics to economics
62P99 Applications of statistics
62J05 Linear regression; mixed models

Software:

Algorithm 39
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References:

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