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A simulated annealing methodology for clusterwise linear regression

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  • Wayne DeSarbo
  • Richard Oliver
  • Arvind Rangaswamy

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Suggested Citation

  • Wayne DeSarbo & Richard Oliver & Arvind Rangaswamy, 1989. "A simulated annealing methodology for clusterwise linear regression," Psychometrika, Springer;The Psychometric Society, vol. 54(4), pages 707-736, September.
    • Handle: RePEc:spr:psycho:v:54:y:1989:i:4:p:707-736
    DOI: 10.1007/BF02296405
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    References listed on IDEAS

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    1. Oliver, Richard L & DeSarbo, Wayne S, 1988. "Response Determinants in Satisfaction Judgments," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 14(4), pages 495-507, March.
    2. Geert Soete & Wayne DeSarbo & J. Carroll, 1985. "Optimal variable weighting for hierarchical clustering: An alternating least-squares algorithm," Journal of Classification, Springer;The Classification Society, vol. 2(1), pages 173-192, December.
    3. Wayne DeSarbo & J. Carroll & Linda Clark & Paul Green, 1984. "Synthesized clustering: A method for amalgamating alternative clustering bases with differential weighting of variables," Psychometrika, Springer;The Psychometric Society, vol. 49(1), pages 57-78, March.
    4. Wayne DeSarbo & William Cron, 1988. "A maximum likelihood methodology for clusterwise linear regression," Journal of Classification, Springer;The Classification Society, vol. 5(2), pages 249-282, September.
    5. Wayne DeSarbo & Vijay Mahajan, 1984. "Constrained classification: The use of a priori information in cluster analysis," Psychometrika, Springer;The Psychometric Society, vol. 49(2), pages 187-215, June.
    6. Green, Paul E & Srinivasan, V, 1978. "Conjoint Analysis in Consumer Research: Issues and Outlook," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 5(2), pages 103-123, Se.
    7. Wayne Desarbo, 1982. "Gennclus: New models for general nonhierarchical clustering analysis," Psychometrika, Springer;The Psychometric Society, vol. 47(4), pages 449-475, December.
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    Citations

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    Cited by:

    1. Ana Oliveira-Brochado & Francisco Vitorino Martins, 2008. "Aspectos Metodológicos da Segmentação de Mercado: Base de Segmentação e Métodos de Classificação," FEP Working Papers 261, Universidade do Porto, Faculdade de Economia do Porto.
    2. Young Woong Park & Yan Jiang & Diego Klabjan & Loren Williams, 2017. "Algorithms for Generalized Clusterwise Linear Regression," INFORMS Journal on Computing, INFORMS, vol. 29(2), pages 301-317, May.
    3. Eva Vande Gaer & Eva Ceulemans & Iven Mechelen & Peter Kuppens, 2012. "The CLASSI-N Method for the Study of Sequential Processes," Psychometrika, Springer;The Psychometric Society, vol. 77(1), pages 85-105, January.
    4. Adil M. Bagirov & Julien Ugon & Hijran G. Mirzayeva, 2015. "Nonsmooth Optimization Algorithm for Solving Clusterwise Linear Regression Problems," Journal of Optimization Theory and Applications, Springer, vol. 164(3), pages 755-780, March.
    5. Tom Frans Wilderjans & Eva Gaer & Henk A. L. Kiers & Iven Mechelen & Eva Ceulemans, 2017. "Principal Covariates Clusterwise Regression (PCCR): Accounting for Multicollinearity and Population Heterogeneity in Hierarchically Organized Data," Psychometrika, Springer;The Psychometric Society, vol. 82(1), pages 86-111, March.
    6. Bagirov, Adil M. & Ugon, Julien & Mirzayeva, Hijran, 2013. "Nonsmooth nonconvex optimization approach to clusterwise linear regression problems," European Journal of Operational Research, Elsevier, vol. 229(1), pages 132-142.
    7. Nazari, Fatemeh & Mohammadian, Abolfazl (Kouros), 2023. "Modeling vehicle-miles of travel accounting for latent heterogeneity," Transport Policy, Elsevier, vol. 133(C), pages 45-53.
    8. Joki, Kaisa & Bagirov, Adil M. & Karmitsa, Napsu & Mäkelä, Marko M. & Taheri, Sona, 2020. "Clusterwise support vector linear regression," European Journal of Operational Research, Elsevier, vol. 287(1), pages 19-35.
    9. Van Aelst, Stefan & (Steven) Wang, Xiaogang & Zamar, Ruben H. & Zhu, Rong, 2006. "Linear grouping using orthogonal regression," Computational Statistics & Data Analysis, Elsevier, vol. 50(5), pages 1287-1312, March.
    10. Ana Oliveira-Brochado & Francisco Vitorino Martins, 2008. "Segmentação de Mercado e modelos mistura de regressão para variáveis normais," FEP Working Papers 262, Universidade do Porto, Faculdade de Economia do Porto.
    11. Seohee Park & Seongeun Kim & Ji Hoon Ryoo, 2020. "Latent Class Regression Utilizing Fuzzy Clusterwise Generalized Structured Component Analysis," Mathematics, MDPI, vol. 8(11), pages 1-16, November.
    12. Réal Carbonneau & Gilles Caporossi & Pierre Hansen, 2014. "Globally Optimal Clusterwise Regression By Column Generation Enhanced with Heuristics, Sequencing and Ending Subset Optimization," Journal of Classification, Springer;The Classification Society, vol. 31(2), pages 219-241, July.
    13. Duncan Fong & Peter Ebbes & Wayne DeSarbo, 2012. "A Heterogeneous Bayesian Regression Model for Cross-sectional Data Involving a Single Observation per Response Unit," Psychometrika, Springer;The Psychometric Society, vol. 77(2), pages 293-314, April.
    14. Carbonneau, Réal A. & Caporossi, Gilles & Hansen, Pierre, 2011. "Globally optimal clusterwise regression by mixed logical-quadratic programming," European Journal of Operational Research, Elsevier, vol. 212(1), pages 213-222, July.
    15. Florian Schreiber, 2017. "Identification of customer groups in the German term life market: a benefit segmentation," Annals of Operations Research, Springer, vol. 254(1), pages 365-399, July.
    16. Hye Suk & Heungsun Hwang, 2010. "Regularized fuzzy clusterwise ridge regression," Advances in Data Analysis and Classification, Springer;German Classification Society - Gesellschaft für Klassifikation (GfKl);Japanese Classification Society (JCS);Classification and Data Analysis Group of the Italian Statistical Society (CLADAG);International Federation of Classification Societies (IFCS), vol. 4(1), pages 35-51, April.
    17. L. A. García‐Escudero & A. Gordaliza & R. San Martín & S. Van Aelst & R. Zamar, 2009. "Robust linear clustering," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 71(1), pages 301-318, January.
    18. Lau, Kin-nam & Leung, Pui-lam & Tse, Ka-kit, 1999. "A mathematical programming approach to clusterwise regression model and its extensions," European Journal of Operational Research, Elsevier, vol. 116(3), pages 640-652, August.
    19. D'Urso, Pierpaolo & Santoro, Adriana, 2006. "Fuzzy clusterwise linear regression analysis with symmetrical fuzzy output variable," Computational Statistics & Data Analysis, Elsevier, vol. 51(1), pages 287-313, November.
    20. Arnaud De Bruyn & John C. Liechty & Eelko K. R. E. Huizingh & Gary L. Lilien, 2008. "Offering Online Recommendations with Minimum Customer Input Through Conjoint-Based Decision Aids," Marketing Science, INFORMS, vol. 27(3), pages 443-460, 05-06.
    21. Michael Brusco & Stephanie Stahl, 2001. "An interactive multiobjective programming approach to combinatorial data analysis," Psychometrika, Springer;The Psychometric Society, vol. 66(1), pages 5-24, March.
    22. Wayne S. DeSarbo & Qian Chen & Ashley Stadler Blank, 2017. "A Parametric Constrained Segmentation Methodology for Application in Sport Marketing," Customer Needs and Solutions, Springer;Institute for Sustainable Innovation and Growth (iSIG), vol. 4(4), pages 37-55, December.
    23. de Ruyter, Ko & Bloemer, Jose & Peeters, Pascal, 1997. "Merging service quality and service satisfaction. An empirical test of an integrative model," Journal of Economic Psychology, Elsevier, vol. 18(4), pages 387-406, June.

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