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.
Monitoring Tools in Robust CWM for the Analysis of Crime Data / A. Cappozzo, L. Garcia-Escudero, F. Greselin, A. Mayo-Iscar (ADVANCES IN INTELLIGENT SYSTEMS AND COMPUTING). - In: Building Bridges between Soft and Statistical Methodologies for Data Science / [a cura di] Luis A. García-Escudero Alfonso Gordaliza Agustín Mayo María Asunción Lubiano Gomez Maria Angeles Gil Przemyslaw Grzegorzewski Olgierd Hryniewicz. - Cham : Springer, 2023. - ISBN 978-3-031-15508-6. - pp. 65-72 (( Intervento presentato al 10. convegno International Conference on Soft Methods in Probability and Statistics (SMPS) tenutosi a Valladolid : 14-16 September nel 2022 [10.1007/978-3-031-15509-3_9].
Monitoring Tools in Robust CWM for the Analysis of Crime Data
A. CappozzoPrimo
;
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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