Erratum to: ``The generalized linear mixed cluster-weighted model

DOI10.1007/s00357-015-9177-zzbMath1335.62100OpenAlexW2265897906MaRDI QIDQ269555

Antonio Punzo, Salvatore Ingrassia, Simona C. Minotti, Giorgio Vittadini

Publication date: 19 April 2016

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

Full work available at URL: https://doi.org/10.1007/s00357-015-9177-z

zbMATH Keywords

model-based clusteringgeneralized linear modelsmixed-type datacluster-weighted models

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Generalized linear models (logistic models) (62J12)

Related Items (2)

Merging components in linear Gaussian cluster-weighted models ⋮ Modeling frequency and severity of claims with the zero-inflated generalized cluster-weighted models

Uses Software

• R
• flexmix
• PGMM
• flexCWM

Cites Work

• Serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models
• Choosing starting values for the EM algorithm for getting the highest likelihood in multivariate Gaussian mixture models
• Choosing initial values for the EM algorithm for finite mixtures
• Model selection and Akaike's information criterion (AIC): The general theory and its analytical extensions
• Estimating the dimension of a model
• The model selection criterion AICu.
• Identifiability of models for clusterwise linear regression
• Model-based clustering via linear cluster-weighted models
• A mixture likelihood approach for generalized linear models
• Clustering and classification via cluster-weighted factor analyzers
• Finite mixtures of unimodal beta and gamma densities and the \(k\)-bumps algorithm
• Finite mixture and Markov switching models.
• Factor analysis with (mixed) observed and latent variables in the exponential family
• Identifiability of finite mixtures of logistic regression models
• Regression and time series model selection in small samples
• Model-Based Gaussian and Non-Gaussian Clustering
• Mixed Poisson Regression Models with Covariate Dependent Rates
• Concomitant variables in finite mixture models
• Flexible mixture modelling with the polynomial Gaussian cluster-weighted model
• On the Spectral Decomposition in Normal Discriminant Analysis
• Identifiability of Finite Mixtures
• Closed Likelihood Ratio Testing Procedures to Assess Similarity of Covariance Matrices
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