Abstract
In this paper we introduce the concept of weak homoscedasticity for covariance matrices of the component densities, in the framework of constrained formulations of the maximum likelihood estimation for mixture models. Further, we give a test for assessing weak homoscedasticity in two sample data. Based on such approach, we present how to implement a constrained EM algorithm for mixtures of t-distributions. The proposal is illustrated on the ground of numerical experiments which show its usefulness in data modeling and classification.
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Acknowledgements
The authors thank the referees for their interesting comments and suggestions which contributed to improving an earlier version of the paper.
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Greselin, F., Ingrassia, S. (2009). Weakly Homoscedastic Constraints for Mixtures of t-Distributions. In: Fink, A., Lausen, B., Seidel, W., Ultsch, A. (eds) Advances in Data Analysis, Data Handling and Business Intelligence. Studies in Classification, Data Analysis, and Knowledge Organization. Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01044-6_20
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DOI: https://doi.org/10.1007/978-3-642-01044-6_20
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