Estimating the Number of Clusters in Logistic Regression Clustering by an Information Theoretic Criterion

Guoqi Qian (), C. Radhakrishna Rao (), Yuehua Wu () and Qing Shao ()
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Guoqi Qian: University of Melbourne, Department of Mathematics and Statistics
C. Radhakrishna Rao: Penn State University, Department of Statistics
Yuehua Wu: York University, Department of Mathematics and Statistics
Qing Shao: Novartis Pharmaceuticals Corporation, Biostatistics and Statistical Reporting

A chapter in Recent Advances in Linear Models and Related Areas, 2008, pp 29-43 from Springer

Abstract: This paper studies the problem of estimating the number of clusters in the context of logistic regression clustering. The classi.cation likelihood approach is employed to tackle this problem. An information theoretic criterion for selecting the number of logistic curves is proposed in the sequel and then its asymptotic property is considered. The paper is arranged as follows: In Section 2, some notations are given and an information theoretic criterion is proposed for estimating the number of clusters. In Section 3, the small sample performance of the proposed criterion is studied by Monte Carlo simulation. In Section 4, the asymptotic property of the criterion proposed in Section 2 is investigated. Some lemmas needed in Section 4 are given in the appendix.

Keywords: Logistic Regression; Binomial Distribution; Maximum Likelihood Estimator; Asymptotic Property; Linear Predictor (search for similar items in EconPapers)
Date: 2008
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-7908-2064-5_2

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DOI: 10.1007/978-3-7908-2064-5_2

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