Inference for multivariate normal mixtures

  title={Inference for multivariate normal mixtures},
  author={Jiahua Chen and Xianming Tan},
  journal={J. Multivariate Analysis},
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 likelihoodbased 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… CONTINUE READING
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Estimating the components of a mixture of normal distributions

N E Day
Biometrika • 1969
View 1 Excerpt
Highly Influenced

Consistency of the constrained maximum likelihood estimator in finite normal mixture models

X Tan, J Chen, R Zhang
Proceedings of the American Statistical Association • 2007

Finite Mixture and Markov Switching Models

S Fruhwirth-Schnatter
Finite Mixture and Markov Switching Models • 2006
View 1 Excerpt

A likelihood-based constrained algorithm for multivariate normal mixture models

S Ingrassia
Statistical Methods & Applications • 2004

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