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A New Family of BAN Estimators for Polytomous Logistic Regression Models based on ϕ- Divergence Measures

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Abstract

In this paper we study polytomous logistic regression model and the asymptotic properties of the minimum ϕ-divergence estimators for this model. A simulation study is conducted to analyze the behavior of these estimators as function of the power-divergence measure ϕ(λ)

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Authors and Affiliations

  1. Department of Mathematics and Statistics, Bowling Green State University, Bowling Green, OH, 43403, USA

    A. K. Gupta, D. Kasturiratna & T. Nguyen

  2. Department of Statistics and O.R., Faculty of Mathematics, Complutense University of Madrid, 28040, Madrid, Spain

    L. Pardo

Authors
  1. A. K. Gupta
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  2. D. Kasturiratna
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  3. T. Nguyen
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  4. L. Pardo
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Corresponding author

Correspondence to A. K. Gupta.

Additional information

Research partially done when was visiting the Bowling Green State University as the Distinguished Lukacs Professor

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Gupta, A.K., Kasturiratna, D., Nguyen, T. et al. A New Family of BAN Estimators for Polytomous Logistic Regression Models based on ϕ- Divergence Measures. Stat. Meth. & Appl. 15, 159–176 (2006). https://doi.org/10.1007/s10260-006-0008-6

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  • DOI: https://doi.org/10.1007/s10260-006-0008-6

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