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

Keywords

Cluster-weighted modeling

OSS delle Nazioni Unite

Questo processo contribuisce al raggiungimento dei seguenti obiettivi di sviluppo sostenibile

Accesso al documento

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

Altri file e collegamenti

Link a IRIS PubliCatt

Cita questo

@inbook{95c603a37eb44ffaa500505068a4b925,

title = "Monitoring Tools in Robust CWM for the Analysis of Crime Data",

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.",

keywords = "Cluster-weighted modeling, Cluster-weighted modeling",

author = "Andrea Cappozzo and LA Garcia-Escudero and F Greselin and A Mayo-Iscar",

year = "2023",

doi = "10.1007/978-3-031-15509-3\_9",

language = "English",

isbn = "978-3-031-15508-6",

volume = "1433",

pages = "65--72",

booktitle = "Building Bridges between Soft and Statistical Methodologies for Data Science",

publisher = "Springer",

}

TY - CHAP

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

N2 - 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.

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

UR - https://publicatt.unicatt.it/handle/10807/309187

U2 - 10.1007/978-3-031-15509-3_9

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

M3 - Chapter

SN - 978-3-031-15508-6

VL - 1433

SP - 65

EP - 72

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

PB - Springer

ER -

Monitoring Tools in Robust CWM for the Analysis of Crime Data

Abstract

Keywords

OSS delle Nazioni Unite

Accesso al documento

Altri file e collegamenti

Fingerprint

Cita questo