A procedure for estimating the number of clusters in logistic regression clustering

DOI10.1007/s00357-009-9035-yzbMath1337.62138OpenAlexW2154906870MaRDI QIDQ265316

Yuehua Wu, Qing Shao, Guoqi Qian

Publication date: 1 April 2016

Published in: Journal of Classification (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1007/s00357-009-9035-y

zbMATH Keywords

asymptoticsmodel selectionlogistic regression clusteringpenalty

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) General nonlinear regression (62J02)

Related Items (1)

Nonsmooth nonconvex optimization approach to clusterwise linear regression problems

Uses Software

• Algorithm 39

Cites Work

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• A Bayesian analysis of the minimum AIC procedure
• A fast algorithm for clusterwise linear regression
• Estimating the dimension of a model
• Clusterwise linear regression
• A strongly consistent information criterion for linear model selection based on \(M\)-estimation
• Model selection with data-oriented penalty
• On model selection via stochastic complexity in robust linear regression
• Law of iterated logarithm and consistent model selection criterion in logistic regression
• A mixture likelihood approach for generalized linear models
• A consistent procedure for determining the number of clusters in regression clustering
• Identifiability of finite mixtures of logistic regression models
• The Use of a Mixture Model in the Analysis of Count Data
• Regression and time series model selection in small samples
• Extending the Akaike Information Criterion to Mixture Regression Models

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