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

被引：6
|
作者：
Qian, Guoqi [1 ]
Wu, Yuehua [2 ]
Shao, Qing [3 ]
机构：
[1] Univ Melbourne, Dept Math & Stat, Melbourne, Vic 3010, Australia
[2] York Univ, Dept Math & Stat, Toronto, ON M3J 1P3, Canada
[3] Novartis Pharmaceut, Biostat & Stat Reporting, E Hanover, NJ 07936 USA
来源：
JOURNAL OF CLASSIFICATION | 2009年 / 26卷 / 02期
关键词：
Asymptotics; Logistic regression clustering; Model selection; Penalty; MODEL SELECTION; LINEAR-REGRESSION; INFORMATION CRITERION; MIXTURE MODEL; LIKELIHOOD;
D O I：
10.1007/s00357-009-9035-y
中图分类号：
O1 [数学];
学科分类号：
0701 ; 070101 ;
摘要：
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.
引用
收藏
页码：183 / 199
页数：17
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