Monitoring Tools in Robust CWM for the Analysis of Crime Data

Andrea Cappozzo; LA Garcia-Escudero; F Greselin; A Mayo-Iscar

doi:10.1007/978-3-031-15509-3_9

Monitoring Tools in Robust CWM for the Analysis of Crime Data

Andrea Cappozzo
, LA Garcia-Escudero
, F Greselin
, A Mayo-Iscar

Risultato della ricerca: Contributo in libro › Capitolo

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.

Lingua originale	Inglese
Titolo della pubblicazione ospite	Building Bridges between Soft and Statistical Methodologies for Data Science
Editore	Springer
Pagine	65-72
Numero di pagine	8
Volume	1433
ISBN (stampa)	978-3-031-15508-6
DOI	https://doi.org/10.1007/978-3-031-15509-3_9
Stato di pubblicazione	Pubblicato - 2023

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Keywords

Cluster-weighted modeling

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10.1007/978-3-031-15509-3_9

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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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T1 - Monitoring Tools in Robust CWM for the Analysis of Crime Data

AU - Cappozzo, Andrea

AU - Garcia-Escudero, LA

AU - Greselin, F

AU - Mayo-Iscar, A

PY - 2023

Y1 - 2023

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AB - 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.

KW - Cluster-weighted modeling

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BT - Building Bridges between Soft and Statistical Methodologies for Data Science

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Monitoring Tools in Robust CWM for the Analysis of Crime Data

Abstract

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Keywords

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