The existing robust methods for model-based classification and clustering deal with elliptically contoured components. Here we introduce robust estimation for mixtures of skew-normal, by the joint usage of trimming and constraints. The model allows to fit heterogeneous skew data with great flexibility.
Garcìa-Escudero, L., Greselin, F., Mayo-Iscar, A. (2015). Robust clustering for heterogeneous skew data. In F. Mola, C. Conversano (a cura di), CLADAG 2015 Book of Abstracts (pp. 154-157). Cagliari : CUEC.
Robust clustering for heterogeneous skew data
Greselin, F;
2015
Abstract
The existing robust methods for model-based classification and clustering deal with elliptically contoured components. Here we introduce robust estimation for mixtures of skew-normal, by the joint usage of trimming and constraints. The model allows to fit heterogeneous skew data with great flexibility.
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