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Minimum phi-divergence estimators for multinomial logistic regression with complex sample design

Author

Listed:
  • Elena Castilla

    (Complutense University of Madrid)

  • Nirian Martín

    (Complutense University of Madrid)

  • Leandro Pardo

    (Complutense University of Madrid)

Abstract

This article develops the theoretical framework needed to study the multinomial regression model for complex sample design with pseudo-minimum phi-divergence estimators. The numerical example and the simulation study propose new estimators for the parameter of the logistic regression with overdispersed multinomial distributions for the response variables, the pseudo-minimum Cressie–Read divergence estimators, as well as new estimators for the intra-cluster correlation coefficient. The simulation study shows that the Binder’s method for the intra-cluster correlation coefficient exhibits an excellent performance when the pseudo-minimum Cressie–Read divergence estimator, with $$\lambda =\frac{2}{3}$$ λ = 2 3 , is plugged.

Suggested Citation

  • Elena Castilla & Nirian Martín & Leandro Pardo, 2018. "Minimum phi-divergence estimators for multinomial logistic regression with complex sample design," AStA Advances in Statistical Analysis, Springer;German Statistical Society, vol. 102(3), pages 381-411, July.
    • Handle: RePEc:spr:alstar:v:102:y:2018:i:3:d:10.1007_s10182-017-0311-6
    DOI: 10.1007/s10182-017-0311-6
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    References listed on IDEAS

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    4. A. Gupta & D. Kasturiratna & T. Nguyen & L. Pardo, 2006. "A New Family of BAN Estimators for Polytomous Logistic Regression Models based on ϕ- Divergence Measures," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 15(2), pages 159-176, August.
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    Cited by:

    1. Elena Castilla & Abhik Ghosh & Nirian Martin & Leandro Pardo, 2021. "Robust semiparametric inference for polytomous logistic regression with complex survey design," 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. 15(3), pages 701-734, September.

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