We consider constrained formulations of the maximum likelihood estimation for mixture models. In this framework, we introduce the concept of weak homoscedasticity for covariance matrices of the component densities and give a test for detecting weak homoscedasticity in two sample data under the multinormal assumption. Based on such concept, we present a constrained EM algorithm for data modeling via mixtures of t-distributions. The proposal is illustrated on the ground of numerical experiments which show the usefulness of the present approach in data modeling.
Greselin, F., Ingrassia, S. (2010). Weakly homoscedastic constraints for mixtures of t-distributions. In A. Fink, B. Lausen, W. Seidel, A. Ultsch (a cura di), Advances in Data Analysis, Data Handling and Business Intelligence (pp. 219-228). Springer, Heidelberg-Berlin [10.1007/978-3-642-01044-6_20].
Weakly homoscedastic constraints for mixtures of t-distributions
GRESELIN, FRANCESCA;
2010
Abstract
We consider constrained formulations of the maximum likelihood estimation for mixture models. In this framework, we introduce the concept of weak homoscedasticity for covariance matrices of the component densities and give a test for detecting weak homoscedasticity in two sample data under the multinormal assumption. Based on such concept, we present a constrained EM algorithm for data modeling via mixtures of t-distributions. The proposal is illustrated on the ground of numerical experiments which show the usefulness of the present approach in data modeling.
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