Root selection in normal mixture models

DOI10.1016/j.csda.2012.01.022zbMath1252.62013OpenAlexW2101974632MaRDI QIDQ693259

Byungtae Seo, Daeyoung Kim

Publication date: 7 December 2012

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

Full work available at URL: https://doi.org/10.1016/j.csda.2012.01.022

zbMATH Keywords

consistencysingularitymaximum likelihoodnormal mixturespurious local maximizer

Mathematics Subject Classification ID

Related Items (22)

Dealing with multiple local modalities in latent class profile analysis ⋮ Editorial: The 2nd special issue on advances in mixture models ⋮ A multivariate linear regression analysis using finite mixtures of \(t\) distributions ⋮ Assessment of the number of components in Gaussian mixture models in the presence of multiple local maximizers ⋮ Semiparametric mixture: continuous scale mixture approach ⋮ Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model ⋮ Finite Mixture of Linear Regression Models: An Adaptive Constrained Approach to Maximum Likelihood Estimation ⋮ Eigenvalues and constraints in mixture modeling: geometric and computational issues ⋮ A data driven equivariant approach to constrained Gaussian mixture modeling ⋮ Probabilistic assessment of model-based clustering ⋮ Discussion on “Distributional independent component analysis for diverse neuroimaging modalities” by Ben Wu, Subhadip Pal, Jian Kang, and Ying Guo ⋮ A simple root selection method for univariate finite normal mixture models ⋮ Merging components in linear Gaussian cluster-weighted models ⋮ Model-based clustering with determinant-and-shape constraint ⋮ Scale-constrained approaches for maximum likelihood estimation and model selection of clusterwise linear regression models ⋮ Root selection in normal mixture models ⋮ Maximizing leave-one-out likelihood for the location parameter of unbounded densities ⋮ Estimation under copula-based Markov normal mixture models for serially correlated data ⋮ Mixtures of multivariate contaminated normal regression models ⋮ The Mixturegram: A Visualization Tool for Assessing the Number of Components in Finite Mixture Models ⋮ Density deconvolution under a \(k\)-monotonicity constraint ⋮ A note on maximum likelihood estimation for mixture models

Uses Software

• MixSim

Cites Work

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• A computational strategy for doubly smoothed MLE exemplified in the normal mixture model
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• Strong consistency of the maximum likelihood estimator for finite mixtures of location-scale distributions when the scale parameters are exponentially small
• Consistency of the Maximum Likelihood Estimator in the Presence of Infinitely Many Incidental Parameters
• Penalized Maximum Likelihood Estimator for Normal Mixtures
• Bayesian Mixture Labeling by Highest Posterior Density
• Note on the Consistency of the Maximum Likelihood Estimate

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