Depending on the selected hyper-parameters, cluster weighted modeling may produce a set of diverse solutions. Particularly, the user can manually specify the number of mixture components, the degree of heteroscedasticity of the clusters in the explanatory variables and of the errors around the regression lines. In addition, when performing robust inference, the level of impartial trimming enforced in the estimation needs to be selected. This flexibility gives rise to a variety of “legitimate” solutions. To mitigate the problem of model selection, we propose a two stage monitoring procedure to identify a set of “good models”. An application to the benchmark tone perception data showcases the benefits of the approach.

Cappozzo, A., Garcìa-Escudero, L., Greselin, F., Mayo-Iscar, A. (2021). Exploring solutions via monitoring for cluster weighted robust models. In G.C. Porzio, C. Rampichini, C. Bocci (a cura di), ClaDAG 2021 Book of Abstracts and Short papers (pp. 284-287). Firenze University Press [10.36253/978-88-5518-340-6].

Exploring solutions via monitoring for cluster weighted robust models

Cappozzo, A;Greselin, F.;
2021

Abstract

Depending on the selected hyper-parameters, cluster weighted modeling may produce a set of diverse solutions. Particularly, the user can manually specify the number of mixture components, the degree of heteroscedasticity of the clusters in the explanatory variables and of the errors around the regression lines. In addition, when performing robust inference, the level of impartial trimming enforced in the estimation needs to be selected. This flexibility gives rise to a variety of “legitimate” solutions. To mitigate the problem of model selection, we propose a two stage monitoring procedure to identify a set of “good models”. An application to the benchmark tone perception data showcases the benefits of the approach.
Capitolo o saggio
Cluster-weighted modeling, Outliers, Trimmed BIC, Eigenvalue constraint, Monitoring, Constrained estimation, Model-based clustering
English
ClaDAG 2021 Book of Abstracts and Short papers
978-88-5518-340-6
Cappozzo, A., Garcìa-Escudero, L., Greselin, F., Mayo-Iscar, A. (2021). Exploring solutions via monitoring for cluster weighted robust models. In G.C. Porzio, C. Rampichini, C. Bocci (a cura di), ClaDAG 2021 Book of Abstracts and Short papers (pp. 284-287). Firenze University Press [10.36253/978-88-5518-340-6].
Cappozzo, A; Garcìa-Escudero, L; Greselin, F; Mayo-Iscar, A
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/335955
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