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
On the upper bound of the number of modes of a multivariate normal mixture
Journal of Multivariate Analysis
Root selection in normal mixture models
Computational Statistics & Data Analysis
Journal of Multivariate Analysis
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Multivariate normal mixtures provide a flexible model for high-dimensional data. They are widely used in statistical genetics, statistical finance, and other disciplines. Due to the unboundedness of the likelihood function, classical likelihood-based methods, which may have nice practical properties, are inconsistent. In this paper, we recommend a penalized likelihood method for estimating the mixing distribution. We show that the maximum penalized likelihood estimator is strongly consistent when the number of components has a known upper bound. We also explore a convenient EM-algorithm for computing the maximum penalized likelihood estimator. Extensive simulations are conducted to explore the effectiveness and the practical limitations of both the new method and the ratified maximum likelihood estimators. Guidelines are provided based on the simulation results.