A Simulated Annealing Methodology for Clusterwise Linear Regression

Psychometrika, Volume 54, Issue 4, pp 707-736, December 1989

30 Pages Posted: 2 Jun 2016

See all articles by Wayne S. DeSarbo

Wayne S. DeSarbo

Pennsylvania State University

Richard L. Oliver

Vanderbilt University - Marketing

Arvind Rangaswamy

Pennsylvania State University - Department of Marketing

Date Written: December 1989

Abstract

In many regression applications, users are often faced with difficulties due to nonlinear relationships, heterogeneous subjects, or time series which are best represented by splines. In such applications, two or more regression functions are often necessary to best summarize the underlying structure of the data. Unfortunately, in most cases, it is not known a priori which subset of observations should be approximated with which specific regression function. This paper presents a methodology which simultaneously clusters observations into a preset number of groups and estimates the corresponding regression functions' coefficients, all to optimize a common objective function. We describe the problem and discuss related procedures. A new simulated annealing-based methodology is described as well as program options to accommodate overlapping or nonoverlapping clustering, replications per subject, univariate or multivariate dependent variables, and constraints imposed on cluster membership. Extensive Monte Carlo analyses are reported which investigate the overall performance of the methodology. A consumer psychology application is provided concerning a conjoint analysis investigation of consumer satisfaction determinants. Finally, other applications and extensions of the methodology are discussed.

Keywords: cluster analysis, combinatorial optimization, regression analysis, simulated annealing, consumer psychology

Suggested Citation

DeSarbo, Wayne S. and Oliver, Richard L. and Rangaswamy, Arvind, A Simulated Annealing Methodology for Clusterwise Linear Regression (December 1989). Psychometrika, Volume 54, Issue 4, pp 707-736, December 1989, Available at SSRN: https://ssrn.com/abstract=2787309

Wayne S. DeSarbo (Contact Author)

Pennsylvania State University ( email )

University Park
State College, PA 16802
United States

Richard L. Oliver

Vanderbilt University - Marketing ( email )

Nashville, TN 37203
United States

Arvind Rangaswamy

Pennsylvania State University - Department of Marketing ( email )

University Park, PA 16802-3306
United States

Do you have a job opening that you would like to promote on SSRN?

Paper statistics

Downloads
8
Abstract Views
278
PlumX Metrics