Modeling Recidivism through Bayesian Regression Models and Deep Neural Networks

Modeling Recidivism through Bayesian Regression Models and Deep Neural Networks

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 regress...

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Bibliographic Details
Main Authors: Rolando de la Cruz, Oslando Padilla, Mauricio A. Valle, Gonzalo A. Ruz
Format: Article
Language:English
Published: MDPI AG 2021-03-01
Series:Mathematics
Subjects:
Cox proportional hazard deep neural network
Cox regression model
cure rate model
logistic regression model
random survival forest
recidivism
Online Access:https://www.mdpi.com/2227-7390/9/6/639

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https://www.mdpi.com/2227-7390/9/6/639

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