Modeling Recidivism through Bayesian Regression Models and Deep Neural Networks

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dc.contributor.authorde la Cruz, Rolando
dc.contributor.authorPadilla, Oslando
dc.contributor.authorValle, Mauricio A.
dc.contributor.authorRuz, Gonzalo A.
dc.date.accessioned2025-01-20T23:50:48Z
dc.date.available2025-01-20T23:50:48Z
dc.date.issued2021
dc.description.abstractThis study aims to analyze and explore criminal recidivism with different modeling strategies: one based on an explanation of the phenomenon and another based on a prediction task. We compared three common statistical approaches for modeling recidivism: the logistic regression model, the Cox regression model, and the cure rate model. The parameters of these models were estimated from a Bayesian point of view. Additionally, for prediction purposes, we compared the Cox proportional model, a random survival forest, and a deep neural network. To conduct this study, we used a real dataset that corresponds to a cohort of individuals which consisted of men convicted of sexual crimes against women in 1973 in England and Wales. The results show that the logistic regression model tends to give more precise estimations of the probabilities of recidivism both globally and with the subgroups considered, but at the expense of running a model for each moment of the time that is of interest. The cure rate model with a relatively simple distribution, such as Weibull, provides acceptable estimations, and these tend to be better with longer follow-up periods. The Cox regression model can provide the most biased estimations with certain subgroups. The prediction results show the deep neural network's superiority compared to the Cox proportional model and the random survival forest.
dc.description.funderANID FONDECYT
dc.fuente.origenWOS
dc.identifier.doi10.3390/math9060639
dc.identifier.eissn2227-7390
dc.identifier.urihttps://doi.org/10.3390/math9060639
dc.identifier.urihttps://repositorio.uc.cl/handle/11534/94743
dc.identifier.wosidWOS:000645321400001
dc.issue.numero6
dc.language.isoen
dc.revistaMathematics
dc.rightsacceso restringido
dc.subjectCox proportional hazard deep neural network
dc.subjectCox regression model
dc.subjectcure rate model
dc.subjectlogistic regression model
dc.subjectrandom survival forest
dc.subjectrecidivism
dc.subject.ods03 Good Health and Well-being
dc.subject.odspa03 Salud y bienestar
dc.titleModeling Recidivism through Bayesian Regression Models and Deep Neural Networks
dc.typeartículo
dc.volumen9
sipa.indexWOS
sipa.trazabilidadWOS;2025-01-12
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