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A generalized regression model for a binary response

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  • Kateri, Maria
  • Agresti, Alan

Abstract

Logistic regression is the closest model, given its sufficient statistics, to the model of constant success probability in terms of Kullback-Leibler information. A generalized binary model has this property for the more general [phi]-divergence. These results generalize to multinomial and other discrete data.

Suggested Citation

  • Kateri, Maria & Agresti, Alan, 2010. "A generalized regression model for a binary response," Statistics & Probability Letters, Elsevier, vol. 80(2), pages 89-95, January.
    • Handle: RePEc:eee:stapro:v:80:y:2010:i:2:p:89-95
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    References listed on IDEAS

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    1. Kateri, Maria & Agresti, Alan, 2007. "A class of ordinal quasi-symmetry models for square contingency tables," Statistics & Probability Letters, Elsevier, vol. 77(6), pages 598-603, March.
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    Cited by:

    1. Kateri, Maria & Nikolov, Nikolay I., 2022. "A generalized Mallows model based on ϕ-divergence measures," Journal of Multivariate Analysis, Elsevier, vol. 190(C).
    2. Tsagris, Michail, 2015. "A novel, divergence based, regression for compositional data," MPRA Paper 72769, University Library of Munich, Germany.

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