A constrained EM algorithm for univariate normal mixtures
Journal of Statistical Computation and Simulation
Robust regression and outlier detection
Robust regression and outlier detection
Hierarchical mixtures of experts and the EM algorithm
Neural Computation
Robust mixture modelling using the t distribution
Statistics and Computing
Clusters, outliers, and regression: fixed point clusters
Journal of Multivariate Analysis
Journal of Multivariate Analysis
Mixtures of regressions with predictor-dependent mixing proportions
Computational Statistics & Data Analysis
Robust clusterwise linear regression through trimming
Computational Statistics & Data Analysis
Model based labeling for mixture models
Statistics and Computing
Robust fitting of mixture regression models
Computational Statistics & Data Analysis
Editorial: The 2nd special issue on advances in mixture models
Computational Statistics & Data Analysis
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The traditional estimation of mixture regression models is based on the normal assumption of component errors and thus is sensitive to outliers or heavy-tailed errors. A robust mixture regression model based on the t-distribution by extending the mixture of t-distributions to the regression setting is proposed. However, this proposed new mixture regression model is still not robust to high leverage outliers. In order to overcome this, a modified version of the proposed method, which fits the mixture regression based on the t-distribution to the data after adaptively trimming high leverage points, is also proposed. Furthermore, it is proposed to adaptively choose the degrees of freedom for the t-distribution using profile likelihood. The proposed robust mixture regression estimate has high efficiency due to the adaptive choice of degrees of freedom.