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A Procedure for Estimating the Number of Clusters in Logistic Regression Clustering

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Abstract

This paper studies the problem of estimating the number of clusters in the context of logistic regression clustering. The classification likelihood approach is employed to tackle this problem. A model-selection based criterion for selecting the number of logistic curves is proposed and its asymptotic property is also considered. The small sample performance of the proposed criterion is studied by Monto Carlo simulation. In addition, a real data example is presented.

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Authors and Affiliations

  1. Department of Mathematics and Statistics, The University of Melbourne, Melbourne, VIC, 3010, Australia

    Guoqi Qian

  2. Department of Mathematics and Statistics, York University, Toronto, ON, M3J 1P3, Canada

    Yuehua Wu

  3. Biostatistics and Statistical Reporting, One Health Plaza, Bldg. 435–4173, Novartis Pharmaceuticals Corporation, East Hanover, NJ, 07936, USA

    Qing Shao

Authors
  1. Guoqi Qian
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  2. Yuehua Wu
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  3. Qing Shao
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Corresponding author

Correspondence to Guoqi Qian.

Additional information

The authors would like to thank the editor, Prof. Willem J. Heiser, and the anonymous referees for the valuable comments and suggestions, which have led to the improvement of this paper.

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Qian, G., Wu, Y. & Shao, Q. A Procedure for Estimating the Number of Clusters in Logistic Regression Clustering. J Classif 26, 183–199 (2009). https://doi.org/10.1007/s00357-009-9035-y

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