Robust inference for the Cluster Weighted Model requires the specification of a few hyper-parameters. Their role is crucial for increasing the quality of the estimators, while arbitrary decisions about their value could severely hamper inferential results. To guide the user in the delicate choice of such parameters, a monitoring approach has been introduced in the recent literature, yielding an adaptive method. The approach is here exemplified, via the analysis of a dataset on the effect of punishment regimes on crime rates.
Cappozzo, A., Garcia-Escudero, L., Greselin, F., Mayo-Iscar, A. (2023). Monitoring Tools in Robust CWM for the Analysis of Crime Data. In L.A. García-Escudero, A. Gordaliza, A. Mayo, M.A. Lubiano Gomez, M. Angeles Gil, P. Grzegorzewski, et al. (a cura di), Building Bridges between Soft and Statistical Methodologies for Data Science . SMPS 2022. Advances in Intelligent Systems and Computing (pp. 65-72). Springer International Publishing [10.1007/978-3-031-15509-3_9].
Monitoring Tools in Robust CWM for the Analysis of Crime Data
Greselin, Francesca
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2023
Abstract
Robust inference for the Cluster Weighted Model requires the specification of a few hyper-parameters. Their role is crucial for increasing the quality of the estimators, while arbitrary decisions about their value could severely hamper inferential results. To guide the user in the delicate choice of such parameters, a monitoring approach has been introduced in the recent literature, yielding an adaptive method. The approach is here exemplified, via the analysis of a dataset on the effect of punishment regimes on crime rates.
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