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Semiparametric estimation of (constrained) ultrametric trees

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  • Wedel, Michel
  • DeSarbo, Wayne S.

    (Groningen University)

Abstract

This paper introduces a general, formal treatment of dynamic constraints, i.e., constraints on the state changes that are allowed in a given state space. Such dynamic constraints can be seen as representations of "real world" constraints in a managerial context. The notions of transition, reversible and irreversible transition, and transition relation will be introduced. The link with Kripke models (for modal logics) is also made explicit. Several (subtle) examples of dynamic constraints will be given. Some important classes of dynamic constraints in a database context will be identified, e.g. various forms of cumulativity, non-decreasing values, constraints on initial and final values, life cycles, changing life cycles, and transition and constant dependencies. Several properties of these dependencies will be treated. For instance, it turns out that functional dependencies can be considered as "degenerated" transition dependencies. Also, the distinction between primary keys and alternate keys is reexamined, from a dynamic point of view.

Suggested Citation

  • Wedel, Michel & DeSarbo, Wayne S., 1996. "Semiparametric estimation of (constrained) ultrametric trees," Research Report 96B34, University of Groningen, Research Institute SOM (Systems, Organisations and Management).
  • Handle: RePEc:gro:rugsom:96b34
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    File URL: http://irs.ub.rug.nl/ppn/155260723
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    References listed on IDEAS

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    1. James Corter & Amos Tversky, 1986. "Extended similarity trees," Psychometrika, Springer;The Psychometric Society, vol. 51(3), pages 429-451, September.
    2. 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.
    3. J. Carroll & Linda Clark & Wayne DeSarbo, 1984. "The representation of three-way proximity data by single and multiple tree structure models," Journal of Classification, Springer;The Classification Society, vol. 1(1), pages 25-74, December.
    4. 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.
    5. 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.
    6. Stephen Johnson, 1967. "Hierarchical clustering schemes," Psychometrika, Springer;The Psychometric Society, vol. 32(3), pages 241-254, September.
    7. Jamshidian, Mortaza & Bentler, Peter M., 1993. "A modified Newton method for constrained estimation in covariance structure analysis," Computational Statistics & Data Analysis, Elsevier, vol. 15(2), pages 133-146, February.
    8. Shmuel Sattath & Amos Tversky, 1977. "Additive similarity trees," Psychometrika, Springer;The Psychometric Society, vol. 42(3), pages 319-345, September.
    9. Rao, Vithala R & Sabavala, Darius Jal, 1981. "Inference in Hierarchical Choice Processes from Panel Data," Journal of Consumer Research, Journal of Consumer Research Inc., vol. 8(1), pages 85-96, June.
    10. Greg M. Allenby, 1989. "A Unified Approach to Identifying, Estimating and Testing Demand Structures with Aggregate Scanner Data," Marketing Science, INFORMS, vol. 8(3), pages 265-280.
    11. J. Carroll, 1976. "Spatial, non-spatial and hybrid models for scaling," Psychometrika, Springer;The Psychometric Society, vol. 41(4), pages 439-463, December.
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