文章基本信息

标题：Parameter Choice, Stability and Validity for Robust Cluster Weighted Modeling
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作者：Andrea Cappozzo ; Luis Angel García Escudero ; Francesca Greselin 等
期刊名称：Stats
电子版ISSN：2571-905X
出版年度：2021
卷号：4
期号：3
页码：602-615
DOI：10.3390/stats4030036
语种：English
出版社：MDPI AG
摘要：Statistical inference based on the cluster weighted model often requires some subjective judgment from the modeler. Many features influence the final solution, such as the number of mixture components, the shape of the clusters in the explanatory variables, and the degree of heteroscedasticity of the errors around the regression lines. Moreover, to deal with outliers and contamination that may appear in the data, hyper-parameter values ensuring robust estimation are also needed. In principle, this freedom gives rise to a variety of “legitimate” solutions, each derived by a specific set of choices and their implications in modeling. Here we introduce a method for identifying a “set of good models” to cluster a dataset, considering the whole panorama of choices. In this way, we enable the practitioner, or the scientist who needs to cluster the data, to make an educated choice. They will be able to identify the most appropriate solutions for the purposes of their own analysis, in light of their stability and validity.