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Modeling Recidivism through Bayesian Regression Models and Deep Neural Networks

Author

Listed:
  • Rolando de la Cruz

    (Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Diagonal Las Torres 2640, Peñalolén, Santiago 7941169, Chile)

  • Oslando Padilla

    (Departamento de Salud Pública, Facultad de Medicina, Pontificia Universidad Católica de Chile, Santiago 8320000, Chile)

  • Mauricio A. Valle

    (Facultad de Economía y Negocios, Universidad Finis Terrae, Santiago 7501015, Chile)

  • Gonzalo A. Ruz

    (Facultad de Ingeniería y Ciencias, Universidad Adolfo Ibáñez, Diagonal Las Torres 2640, Peñalolén, Santiago 7941169, Chile
    Center of Applied Ecology and Sustainability (CAPES), Santiago 8331150, Chile)

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.

Suggested Citation

  • Rolando de la Cruz & Oslando Padilla & Mauricio A. Valle & Gonzalo A. Ruz, 2021. "Modeling Recidivism through Bayesian Regression Models and Deep Neural Networks," Mathematics, MDPI, vol. 9(6), pages 1-14, March.
  • Handle: RePEc:gam:jmathe:v:9:y:2021:i:6:p:639-:d:519000
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    References listed on IDEAS

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    2. Piquero, Alex R., 2000. "Assessing the relationships between gender, chronicity, seriousness, and offense skewness in criminal offending," Journal of Criminal Justice, Elsevier, vol. 28(2), pages 103-115.
    3. Herman J. Bierens & Jose R. Carvalho, 2007. "Semi-nonparametric competing risks analysis of recidivism," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 22(5), pages 971-993.
    4. Palocsay, Susan W. & Wang, Ping & Brookshire, Robert G., 2000. "Predicting criminal recidivism using neural networks," Socio-Economic Planning Sciences, Elsevier, vol. 34(4), pages 271-284, December.
    5. Martin, Andrew D. & Quinn, Kevin M. & Park, Jong Hee, 2011. "MCMCpack: Markov Chain Monte Carlo in R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 42(i09).
    6. Hoffman, Peter B. & Stone-Meierhoefer, Barbara, 1980. "Reporting recidivism rates: The criterion and follow-up issues," Journal of Criminal Justice, Elsevier, vol. 8(1), pages 53-60.
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    1. DeJante EATON & Jack PHAN, 2023. "Examining Bias-Sentencing and Recidivism of Minorities in South Texas: A Case Study Data Analysis," RAIS Journal for Social Sciences, Research Association for Interdisciplinary Studies, vol. 7(1), pages 21-28, June.

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