On the estimation of mixtures of Poisson regression models with large number of components

DOI10.1016/j.csda.2014.07.005zbMath1468.62154OpenAlexW2015286907MaRDI QIDQ109443

Cathy Maugis-Rabusseau, Panagiotis Papastamoulis, Marie-Laure Martin-Magniette, Cathy Maugis-Rabusseau, Marie-Laure Martin Magniette

Publication date: January 2016

Published in: Computational Statistics & Data Analysis, Computational Statistics and Data Analysis (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1016/j.csda.2014.07.005

zbMATH Keywords

clusteringmixtures of distributionsEM algorithm initializationmultimodal likelihood

Mathematics Subject Classification ID

Computational methods for problems pertaining to statistics (62-08) Classification and discrimination; cluster analysis (statistical aspects) (62H30)

Related Items (7)

Editorial: The third special issue on advances in mixture models ⋮ Hierarchical mixture-of-experts models for count variables with excessive zeros ⋮ Copula-based bivariate finite mixture regression models with an application for insurance claim count data ⋮ Overfitting Bayesian mixtures of factor analyzers with an unknown number of components ⋮ EM for mixtures ⋮ Fuzzy weighted \(c\)-harmonic regressions clustering algorithm ⋮ poisson.glm.mix

Uses Software

• R
• flexmix
• DEseq
• poisson.glm.mix

Cites Work

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• 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
• Estimating the dimension of a model
• The distribution of the likelihood ratio for mixtures of densities from the one-parameter exponential family
• Zero-inflated generalized Poisson regression mixture model for mapping quantitative trait loci underlying count trait with many zeros
• Finite mixture and Markov switching models.
• Zero-Inflated Poisson Regression, with an Application to Defects in Manufacturing
• Nonparametric Maximum Likelihood Estimation of a Mixing Distribution
• Mixed Poisson Regression Models with Covariate Dependent Rates
• Finite mixture models
• Handling the Label Switching Problem in Latent Class Models Via the ECR Algorithm

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