IDEAS home Printed from https://ideas.repec.org/a/ers/ijfirm/v4y2014i4p812.html
   My bibliography  Save this article

Identifying Small Market Segments with Mixture Regression Models

Author

Listed:
  • Ana Oliveira-Brochado
  • Francisco Vitorino Martins

Abstract

The purpose of this work is to determine how well criteria designed to help the selection of the adequate number of market segments perform in recovering small niche market segments, in mixture regressions of normal data. As in real world data the true number of market segments is unknown, the results of this study are based on experimental data. The simulation experiment compares 27 segment retention criteria, comprising 14 information criteria and 13 classification-based criteria. The results reveal that AIC3, AIC4, HQ, BIC, CAIC, ICLBIC and ICOMPLBIC are the best criteria in recovering small niche segments and encourage its use.

Suggested Citation

  • Ana Oliveira-Brochado & Francisco Vitorino Martins, 2014. "Identifying Small Market Segments with Mixture Regression Models," International Journal of Finance, Insurance and Risk Management, International Journal of Finance, Insurance and Risk Management, vol. 4(4), pages 812-812.
    • Handle: RePEc:ers:ijfirm:v:4:y:2014:i:4:p:812
    as

    Download full text from publisher

    File URL: https://journalfirm.com/journal/111/download
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Wayne S. DeSarbo & Kamel Jedidi & Indrajit Sinha, 2001. "Customer value analysis in a heterogeneous market," Strategic Management Journal, Wiley Blackwell, vol. 22(9), pages 845-857, September.
    2. Gilles Celeux & Gilda Soromenho, 1996. "An entropy criterion for assessing the number of clusters in a mixture model," Journal of Classification, Springer;The Classification Society, vol. 13(2), pages 195-212, September.
    3. Hawkins, Dollena S. & Allen, David M. & Stromberg, Arnold J., 2001. "Determining the number of components in mixtures of linear models," Computational Statistics & Data Analysis, Elsevier, vol. 38(1), pages 15-48, November.
    4. Hamparsum Bozdogan, 1987. "Model selection and Akaike's Information Criterion (AIC): The general theory and its analytical extensions," Psychometrika, Springer;The Psychometric Society, vol. 52(3), pages 345-370, September.
    5. 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.
    6. Michel Wedel & Wayne DeSarbo, 1995. "A mixture likelihood approach for generalized linear models," Journal of Classification, Springer;The Classification Society, vol. 12(1), pages 21-55, March.
    7. Chih-Chien Yang & Chih-Chiang Yang, 2007. "Separating Latent Classes by Information Criteria," Journal of Classification, Springer;The Classification Society, vol. 24(2), pages 183-203, September.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Nicolas Depraetere & Martina Vandebroek, 2014. "Order selection in finite mixtures of linear regressions," Statistical Papers, Springer, vol. 55(3), pages 871-911, August.
    2. Ana Oliveira-Brochado & Francisco Vitorino Martins, 2008. "Determining the Number of Market Segments Using an Experimental Design," FEP Working Papers 263, Universidade do Porto, Faculdade de Economia do Porto.
    3. Ana Oliveira-Brochado & F. Vitorino Martins, 2006. "Examining the segment retention problem for the “Group Satellite” case," FEP Working Papers 220, Universidade do Porto, Faculdade de Economia do Porto.
    4. Michele Battisti & Filippo Belloc & Massimo Del Gatto, 2015. "Unbundling Technology Adoption and tfp at the Firm Level: Do Intangibles Matter?," Journal of Economics & Management Strategy, Wiley Blackwell, vol. 24(2), pages 390-414, June.
    5. 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.
    6. David F. Larcker & Scott A. Richardson, 2004. "Fees Paid to Audit Firms, Accrual Choices, and Corporate Governance," Journal of Accounting Research, Wiley Blackwell, vol. 42(3), pages 625-658, June.
    7. Pennings, Joost M. E. & Garcia, Philip, 2004. "Hedging behavior in small and medium-sized enterprises: The role of unobserved heterogeneity," Journal of Banking & Finance, Elsevier, vol. 28(5), pages 951-978, May.
    8. Teague R. Henry & Kathleen M. Gates & Mitchell J. Prinstein & Douglas Steinley, 2020. "Modeling Heterogeneous Peer Assortment Effects Using Finite Mixture Exponential Random Graph Models," Psychometrika, Springer;The Psychometric Society, vol. 85(1), pages 8-34, March.
    9. Michel Wedel, 2001. "Computing the Standards Errors of Mixture Model Parameters with EM when Classes are Well Separated," Computational Statistics, Springer, vol. 16(4), pages 539-558, December.
    10. Sarstedt, Marko & Salcher, André, 2007. "Modellselektion in Finite Mixture PLS-Modellen," Discussion Papers in Business Administration 1394, University of Munich, Munich School of Management.
    11. Pennings, Joost M.E. & Garcia, Philip & Irwin, Scott H. & Good, Darrel L., 2003. "How To Group Market Participants? Heterogeneity In Hedging Behavior," 2003 Annual meeting, July 27-30, Montreal, Canada 21963, American Agricultural Economics Association (New Name 2008: Agricultural and Applied Economics Association).
    12. Heungsun Hwang & Marc Tomiuk, 2010. "Fuzzy clusterwise quasi-likelihood generalized linear models," 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(4), pages 255-270, December.
    13. Salvatore Ingrassia & Antonio Punzo & Giorgio Vittadini & Simona Minotti, 2015. "Erratum to: The Generalized Linear Mixed Cluster-Weighted Model," Journal of Classification, Springer;The Classification Society, vol. 32(2), pages 327-355, July.
    14. Salvatore Ingrassia & Simona Minotti & Giorgio Vittadini, 2012. "Local Statistical Modeling via a Cluster-Weighted Approach with Elliptical Distributions," Journal of Classification, Springer;The Classification Society, vol. 29(3), pages 363-401, October.
    15. Simon Blanchard & Wayne DeSarbo, 2013. "A New Zero-Inflated Negative Binomial Methodology for Latent Category Identification," Psychometrika, Springer;The Psychometric Society, vol. 78(2), pages 322-340, April.
    16. Salvatore Ingrassia & Antonio Punzo, 2020. "Cluster Validation for Mixtures of Regressions via the Total Sum of Squares Decomposition," Journal of Classification, Springer;The Classification Society, vol. 37(2), pages 526-547, July.
    17. Gianfranco Di Vaio & Kerstin Enflo, 2009. "Did Globalization Lead to Segmentation? Identifying Cross-Country Growth Regimes in the Long-Run," Discussion Papers 09-08, University of Copenhagen. Department of Economics.
    18. Chen, Cathy W.S. & Chan, Jennifer S.K. & So, Mike K.P. & Lee, Kevin K.M., 2011. "Classification in segmented regression problems," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2276-2287, July.
    19. Martínez-Zarzoso, Inmaculada & Maruotti, Antonello, 2011. "The impact of urbanization on CO2 emissions: Evidence from developing countries," Ecological Economics, Elsevier, vol. 70(7), pages 1344-1353, May.
    20. Bressolles, Grégory & Durrieu, François & Senecal, Sylvain, 2014. "A consumer typology based on e-service quality and e-satisfaction," Journal of Retailing and Consumer Services, Elsevier, vol. 21(6), pages 889-896.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:ers:ijfirm:v:4:y:2014:i:4:p:812. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Marios Agiomavritis (email available below). General contact details of provider: https://journalfirm.com/ .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.