Exploring solutions via monitoring for cluster weighted robust models

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.