Robust mixture modelling using the t distribution
Statistics and Computing
Assessing a Mixture Model for Clustering with the Integrated Completed Likelihood
IEEE Transactions on Pattern Analysis and Machine Intelligence
Computational Statistics & Data Analysis
Extension of the mixture of factor analyzers model to incorporate the multivariate t-distribution
Computational Statistics & Data Analysis
Model-based clustering with non-elliptically contoured distributions
Statistics and Computing
Intermediate Probability: A Computational Approach
Intermediate Probability: A Computational Approach
Model-based cluster and discriminant analysis with the MIXMOD software
Computational Statistics & Data Analysis
Computational Statistics & Data Analysis
Constrained monotone EM algorithms for mixtures of multivariate t distributions
Statistics and Computing
Extending mixtures of multivariate t-factor analyzers
Statistics and Computing
Multivariate linear regression with non-normal errors: a solution based on mixture models
Statistics and Computing
Initializing the EM algorithm in Gaussian mixture models with an unknown number of components
Computational Statistics & Data Analysis
Root selection in normal mixture models
Computational Statistics & Data Analysis
Computational aspects of fitting mixture models via the expectation-maximization algorithm
Computational Statistics & Data Analysis
Acceleration of the EM algorithm: P-EM versus epsilon algorithm
Computational Statistics & Data Analysis
Editorial: The 2nd special issue on advances in mixture models
Computational Statistics & Data Analysis
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Recently, finite mixture models have been used to model the distribution of the error terms in multivariate linear regression analysis. In particular, Gaussian mixture models have been employed. A novel approach that assumes that the error terms follow a finite mixture of t distributions is introduced. This assumption allows for an extension of multivariate linear regression models, making these models more versatile and robust against the presence of outliers in the error term distribution. The issues of model identifiability and maximum likelihood estimation are addressed. In particular, identifiability conditions are provided and an Expectation-Maximisation algorithm for estimating the model parameters is developed. Properties of the estimators of the regression coefficients are evaluated through Monte Carlo experiments and compared to the estimators from the Gaussian mixture models. Results from the analysis of two real datasets are presented.