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Divergence-Based Estimation and Testing of Statistical Models of Classification

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  • Menendez, M.
  • Morales, D.
  • Pardo, L.
  • Vajda, I.
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    Abstract

    The problems of estimating parameters of statistical models for categorical data, and testing hypotheses about these models are studied. Asymptotic properties of estimators minimizing [phi]-divergence between theoretical and empirical vectors of means are established. Asymptotic distributions of [phi]-divergences between empirical and estimated vectors of means are explictly evaluated, and tests based on these statistics are studied. The paper extends results previously established in this area.

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    Article provided by Elsevier in its journal Journal of Multivariate Analysis.

    Volume (Year): 54 (1995)
    Issue (Month): 2 (August)
    Pages: 329-354

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    Handle: RePEc:eee:jmvana:v:54:y:1995:i:2:p:329-354

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    Cited by:
    1. Morales, D. & Pardo, L. & Vajda, I., 2003. "Asymptotic laws for disparity statistics in product multinomial models," Journal of Multivariate Analysis, Elsevier, vol. 85(2), pages 335-360, May.
    2. Leorato, S., 2009. "A refined Jensen's inequality in Hilbert spaces and empirical approximations," Journal of Multivariate Analysis, Elsevier, vol. 100(5), pages 1044-1060, May.
    3. Morales, D. & Pardo, L. & Vajda, I., 1997. "Some New Statistics for Testing Hypotheses in Parametric Models, ," Journal of Multivariate Analysis, Elsevier, vol. 62(1), pages 137-168, July.
    4. Wegenkittl, Stefan, 2002. "A Generalized [phi]-Divergence for Asymptotically Multivariate Normal Models," Journal of Multivariate Analysis, Elsevier, vol. 83(2), pages 288-302, November.

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