Computational aspects of fitting mixture models via the expectation-maximization algorithm

DOI10.1016/j.csda.2012.05.011zbMath1255.62180OpenAlexW2024012039WikidataQ63214076 ScholiaQ63214076MaRDI QIDQ1927163

Isobel Claire Gormley, Thomas Brendan Murphy, Adrian O'Hagan

Publication date: 30 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.2012.05.011

zbMATH Keywords

mclusthierarchical clusteringmodel-based clusteringconvergence ratemultimodal likelihood

Mathematics Subject Classification ID

Classification and discrimination; cluster analysis (statistical aspects) (62H30) Point estimation (62F10)

Related Items

Cox proportional hazards models with frailty for negatively correlated employment processes, Model-based clustering of high-dimensional data: a review, A multivariate linear regression analysis using finite mixtures of \(t\) distributions, Multivariate methods using mixtures: correspondence analysis, scaling and pattern-detection, Initializing the EM Algorithm for Univariate Gaussian, Multi-Component, Heteroscedastic Mixture Models by Dynamic Programming Partitions, Model based clustering for mixed data: clustMD, Eigenvalues and constraints in mixture modeling: geometric and computational issues, Finite mixtures of multivariate skew \(t\)-distributions: some recent and new results, Flexible mixture modelling using the multivariate skew-\(t\)-normal distribution, Extending mixtures of factor models using the restricted multivariate skew-normal distribution, A structured Dirichlet mixture model for compositional data: inferential and applicative issues, Improved model-based clustering performance using Bayesian initialization averaging, Variable selection methods for model-based clustering, Discretization-based direct random sample generation, Consistency of variational Bayes inference for estimation and model selection in mixtures, Retrospective Bayesian outlier detection in INGARCH series, A dynamic stochastic blockmodel for interaction lengths, Finite sample properties of parametric MMD estimation: robustness to misspecification and dependence

Uses Software

• mclust
• R
• MixSim
• PGMM
• Latent GOLD

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Serial and parallel implementations of model-based clustering via parsimonious Gaussian mixture models
• R
• Model-based classification via mixtures of multivariate \(t\)-distributions
• Editorial: Recent developments in mixture models (Hamburg, July 2001)
• 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
• Editorial: Advances in mixture models
• Mixtures of exponential distributions
• Estimating the dimension of a model
• The distribution of the likelihood ratio for mixtures of densities from the one-parameter exponential family
• Efficient approximations for the marginal likelihood of Bayesian networks with hidden variables
• Breakdown points for maximum likelihood estimators of location-scale mixtures
• MCLUST: Software for model-based cluster analysis
• The topography of multivariate normal mixtures
• Types of likelihood maxima in mixture models and their implication on the performance of tests
• On the Bumpy Road to the Dominant Mode
• Maximum likelihood estimation via the ECM algorithm: A general framework
• Mixture Densities, Maximum Likelihood and the EM Algorithm
• Density Estimation With Confidence Sets Exemplified by Superclusters and Voids in the Galaxies
• Model-Based Gaussian and Non-Gaussian Clustering
• Regularized Gaussian Discriminant Analysis Through Eigenvalue Decomposition