A Selective Overview and Comparison of Robust Mixture Regression Estimators

DOI10.1111/insr.12349OpenAlexW2990470437WikidataQ126668659 ScholiaQ126668659MaRDI QIDQ6064345

Weixin Yao, Chun Yu, Guangren Yang

Publication date: 12 December 2023

Published in: International Statistical Review (Search for Journal in Brave)

Full work available at URL: https://doi.org/10.1111/insr.12349

zbMATH Keywords

outliersEM algorithmrobust mixture modellingrobust mixture regression

Mathematics Subject Classification ID

Linear inference, regression (62Jxx) Parametric inference (62Fxx) Multivariate analysis (62Hxx)

Cites Work

• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Unnamed Item
• Nearly unbiased variable selection under minimax concave penalty
• Robust mixture modeling based on scale mixtures of skew-normal distributions
• Robust fitting of mixtures using the trimmed likelihood estimator
• Robust fitting of mixture regression models
• Robust mixture regression model fitting by Laplace distribution
• A profile likelihood method for normal mixture with unequal variance
• Robust estimation of the number of components for mixtures of linear regression models
• Outlier detection and robust mixture modeling using nonconvex penalized likelihood
• Robust estimation of mixtures of regressions with random covariates, via trimming and constraints
• Model based labeling for mixture models
• Hypothesis test for normal mixture models: the EM approach
• 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
• A constrained formulation of maximum-likelihood estimation for normal mixture distributions
• Consistent estimation of a mixing distribution
• A Markov model for switching regressions
• Identifiability of models for clusterwise linear regression
• Hierarchical mixtures-of-experts for exponential family regression models: Approximation and maximum likelihood estimation
• Robust mixture regression using the \(t\)-distribution
• The robust EM-type algorithms for log-concave mixtures of regression models
• Maximum likelihood estimation of the mixture of log-concave densities
• Consistent estimation of mixture complexity.
• Robust clustering in regression analysis via the contaminated Gaussian cluster-weighted model
• Robust clusterwise linear regression through trimming
• Finite mixture and Markov switching models.
• Robust mixture regression modeling based on scale mixtures of skew-normal distributions
• Maximum likelihood estimation via the ECM algorithm: A general framework
• Robust Mixture of Linear Regression Models
• Least Median of Squares Regression
• Robust Estimation of Mixture Complexity
• Robust Linear Clustering
• Model Selection in High Dimensions: A Quadratic-Risk-Based Approach
• Ideal spatial adaptation by wavelet shrinkage
• The ECME algorithm: A simple extension of EM and ECM with faster monotone convergence
• Variable Selection via Nonconcave Penalized Likelihood and its Oracle Properties
• Dealing With Label Switching in Mixture Models
• Computational and Inferential Difficulties with Mixture Posterior Distributions
• Mixture Models, Robustness, and the Weighted Likelihood Methodology
• Penalized minimum‐distance estimates in finite mixture models
• A new method for robust mixture regression
• Robust mixture regression modeling using the least trimmed squares (LTS)-estimation method
• Wavelets in statistics: A review
• Bayesian Mixture Labeling by Highest Posterior Density
• Testing the Order of a Finite Mixture
• Order Selection in Finite Mixture Models With a Nonsmooth Penalty
• Robust Statistics
• Determining the number of components in mixtures of linear models.