Editorial: Advances in Mixture Models
Computational Statistics & Data Analysis
Constrained monotone EM algorithms for mixtures of multivariate t distributions
Statistics and Computing
A computational strategy for doubly smoothed MLE exemplified in the normal mixture model
Computational Statistics & Data Analysis
Degeneracy of the EM algorithm for the MLE of multivariate Gaussian mixtures and dynamic constraints
Computational Statistics & Data Analysis
Root selection in normal mixture models
Computational Statistics & Data Analysis
Multivariate mixture modeling using skew-normal independent distributions
Computational Statistics & Data Analysis
A fast algorithm for robust constrained clustering
Computational Statistics & Data Analysis
A constrained robust proposal for mixture modeling avoiding spurious solutions
Advances in Data Analysis and Classification
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The likelihood function for normal multivariate mixtures may present both local spurious maxima and also singularities and the latter may cause the failure of the optimization algorithms. Theoretical results assure that imposing some constraints on the eigenvalues of the covariance matrices of the multivariate normal components leads to a constrained parameter space with no singularities and at least a smaller number of local maxima of the likelihood function. Conditions assuring that an EM algorithm implementing such constraints maintains the monotonicity property of the usual EM algorithm are provided. Different approaches are presented and their performances are evaluated and compared using numerical experiments.