Cluster-weighted modeling (CWM) is a mixture approach to modeling the joint probability of data coming from a heterogeneous population. CWM includes mixtures of distributions and mixtures of regressions as special cases. The addition of trimming and constraints to the Gaussian CWM estimation methodology is shown to be useful in order to provide robustness and to avoid not only the singularities of the objective function, but also the appearance of spurious solutions. We give theoretical results on existence and consistency and we study the robustness properties of the proposed methodology
Garcian Escudero, L., Gordaliza, A., Greselin, F., Ingrassia, S., Mayo Iscar, A. (2013). Robustness and asymptotics properties of trimmed cluster-weighted restricted modeling. In E.R.a.A.Y. Xuming He (a cura di), Book of Abstract CFE-ERCIM on Computational and Methodological Statistics 2013 (pp. 67-67). Queen Mary, University of London.
Robustness and asymptotics properties of trimmed cluster-weighted restricted modeling
GRESELIN, FRANCESCA;
2013
Abstract
Cluster-weighted modeling (CWM) is a mixture approach to modeling the joint probability of data coming from a heterogeneous population. CWM includes mixtures of distributions and mixtures of regressions as special cases. The addition of trimming and constraints to the Gaussian CWM estimation methodology is shown to be useful in order to provide robustness and to avoid not only the singularities of the objective function, but also the appearance of spurious solutions. We give theoretical results on existence and consistency and we study the robustness properties of the proposed methodology
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