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Author (up) de la Cruz, R.; Padilla, O.; Valle, MA.; Ruz, G.A. doi  openurl
  Title Modeling Recidivism through Bayesian Regression Models and Deep Neural Networks Type
  Year 2021 Publication Mathematics Abbreviated Journal Mathematics  
  Volume 9 Issue 6 Pages 639  
  Keywords Cox proportional hazard deep neural network; Cox regression model; cure rate model; logistic regression model; random survival forest; recidivism  
  Abstract This 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.  
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  Series Volume Series Issue Edition  
  ISSN 2227-7390 ISBN Medium  
  Area Expedition Conference  
  Notes WOS:000645321400001 Approved  
  Call Number UAI @ alexi.delcanto @ Serial 1375  
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