A constrained EM algorithm for univariate normal mixtures
Journal of Statistical Computation and Simulation
Constrained monotone EM algorithms for finite mixture of multivariate Gaussians
Computational Statistics & Data Analysis
Bayesian Regularization for Normal Mixture Estimation and Model-Based Clustering
Journal of Classification
Inference for multivariate normal mixtures
Journal of Multivariate Analysis
A computational strategy for doubly smoothed MLE exemplified in the normal mixture model
Computational Statistics & Data Analysis
Dealing with multiple local modalities in latent class profile analysis
Computational Statistics & Data Analysis
Editorial: The 2nd special issue on advances in mixture models
Computational Statistics & Data Analysis
A multivariate linear regression analysis using finite mixtures of t distributions
Computational Statistics & Data Analysis
Journal of Multivariate Analysis
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Finite mixtures of normal distributions are attractive in identifying the underlying group structure in the data. However, it is a challenging task to do statistical inference in normal mixture models using the method of maximum likelihood, due to the unbounded likelihood and the existence of multiple roots to the likelihood equation including a so-called spurious root. In this article we propose a new likelihood-based method for selecting a statistically reasonable root when there exist multiple roots of the likelihood equation for a finite normal mixture model. We first prove that our proposed methodology can choose a root to the mixture likelihood equation with consistency. We then show, by simulation studies and real examples, that the proposed methods can greatly reduce the risk of choosing problematic roots that have the same features as spurious roots.