Por favor, use este identificador para citar o enlazar este ítem:https://uvadoc.uva.es/handle/10324/74477
Título
Graphical and computational tools to guide parameter choice for the cluster weighted robust model
Año del Documento
2023
Editorial
Taylor and Francis Ltd
Documento Fuente
Cappozzo, A., García-Escudero, L. A., Greselin, F., & Mayo-Iscar, A. (2023). Graphical and Computational Tools to Guide Parameter Choice for the Cluster Weighted Robust Model. Journal of Computational and Graphical Statistics, 32(3), 1195-1214.
Abstract
The Cluster Weighted Robust Model (CWRM) is a recently introduced methodology to robustly estimate mixtures of regressions with random covariates. The CWRM allows users to flexibly perform regression clustering, safeguarding it against data contamination and spurious solutions. Nonetheless, the resulting solution depends on the chosen number of components in the mixture, the percentage of impartial trimming, the degree of heteroscedasticity of the errors around the regression lines and of the clusters in the explanatory variables. Therefore an appropriate model selection is crucially required. Such a complex modeling task may generate several “legitimate” solutions: each one derived from a distinct hyper-parameters specification. The present paper introduces a two step-monitoring procedure to help users
ffectively explore such a vast model space. The first phase uncovers the most appropriate percentages of trimming, whilst the second phase explores the whole set of solutions, conditioning on the outcome derived from the previous step. The final output singles out a set of “top” solutions, whose optimality, stability and validity is assessed. Novel graphical and computational tools - specifically tailored for the CWRM framework - will help the user make an educated choice among the optimal solutions. Three examples on real datasets showcase our proposal in action. Supplementary files for this article are available online.
Revisión por pares
SI
Patrocinador
Spanish Ministerio de Ciencia e Innovación, grant PID2021-128314NB-I00.
Research Programme: “Integration between study design and data analytics for generating credible evidence in the field of healthcare from heterogeneous sources of structured and unstructured data”.
Francesca Greselin work is supported by Milano-Bicocca University Fund for Scientific Research, 2019-ATE-0076.
Research Programme: “Integration between study design and data analytics for generating credible evidence in the field of healthcare from heterogeneous sources of structured and unstructured data”.
Francesca Greselin work is supported by Milano-Bicocca University Fund for Scientific Research, 2019-ATE-0076.
Version del Editor
Idioma
eng
Tipo de versión
info:eu-repo/semantics/draft
Derechos
openAccess
Collections
Files in this item