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Examining the segment retention problem for the “Group Satellite” case

Listed author(s):
  • Ana Oliveira-Brochado


    (Faculdade de Economia do Porto, Universidade do Porto)

  • F. Vitorino Martins


    (Faculdade de Economia do Porto, Universidade do Porto)

Registered author(s):

    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 segments, in mixture regressions of normal data, with experimental data. The simulation experiment compares several segment retention criteria, including information criteria and classification-based criteria. We also address the impact of distributional misspecification on segment retention criteria success rates. This study shows that Akaike’s Information criterion with penalty factors of 3 and 4, rather than the traditional value of 2, are the best segment retention criteria to use in recovering small niche segments. Although these criteria were designed for the specific context of mixture models, they are rarely applied in the marketing literature.

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    Paper provided by Universidade do Porto, Faculdade de Economia do Porto in its series FEP Working Papers with number 220.

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    Length: 16 pages
    Date of creation: Jul 2006
    Handle: RePEc:por:fepwps:220
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    1. 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.
    2. Ana Oliveira-Brochado & Francisco Vitorino Martins, 2005. "Assessing the Number of Components in Mixture Models: a Review," FEP Working Papers 194, Universidade do Porto, Faculdade de Economia do Porto.
    3. Venkatram Ramaswamy & Wayne S. Desarbo & David J. Reibstein & William T. Robinson, 1993. "An Empirical Pooling Approach for Estimating Marketing Mix Elasticities with PIMS Data," Marketing Science, INFORMS, vol. 12(1), pages 103-124.
    4. 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.
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