IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v9y2021i6p639-d519000.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/9/6/639/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/2227-7390/9/6/639/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. 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.
    2. Schmidt, Peter & Witte, Ann Dryden, 1989. "Predicting criminal recidivism using 'split population' survival time models," Journal of Econometrics, Elsevier, vol. 40(1), pages 141-159, January.
    3. 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.
    4. 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).
    5. 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.
    6. 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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    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.
    2. Julia R. Falconer & Eibe Frank & Devon L. L. Polaschek & Chaitanya Joshi, 2024. "Eliciting Informative Priors by Modeling Expert Decision Making," Decision Analysis, INFORMS, vol. 21(2), pages 77-90, June.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Polo, Yolanda & Sese, F. Javier & Verhoef, Peter C., 2011. "The Effect of Pricing and Advertising on Customer Retention in a Liberalizing Market," Journal of Interactive Marketing, Elsevier, vol. 25(4), pages 201-214.
    2. Benjamin Monnery, 2015. "The determinants of recidivism among ex-prisoners: a survival analysis on French data," European Journal of Law and Economics, Springer, vol. 39(1), pages 37-56, February.
    3. Christopher J. W. Zorn, 1998. "An Analytic and Empirical Examination of Zero-Inflated and Hurdle Poisson Specifications," Sociological Methods & Research, , vol. 26(3), pages 368-400, February.
    4. Caroline Krafft, 2020. "Why is fertility on the rise in Egypt? The role of women’s employment opportunities," Journal of Population Economics, Springer;European Society for Population Economics, vol. 33(4), pages 1173-1218, October.
    5. Göhlmann, Silja, 2007. "The Determinants of Smoking Initiation - Empirical Evidence for Germany," Ruhr Economic Papers 27, RWI - Leibniz-Institut für Wirtschaftsforschung, Ruhr-University Bochum, TU Dortmund University, University of Duisburg-Essen.
    6. Bakar, Khandoker Shuvo & Sahu, Sujit K., 2015. "spTimer: Spatio-Temporal Bayesian Modeling Using R," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 63(i15).
    7. Harris, Christopher J., 2012. "The Residual Career Patterns of Police Misconduct," Journal of Criminal Justice, Elsevier, vol. 40(4), pages 323-332.
    8. Baştürk, Nalan & Grassi, Stefano & Hoogerheide, Lennart & Opschoor, Anne & van Dijk, Herman K., 2017. "The R Package MitISEM: Efficient and Robust Simulation Procedures for Bayesian Inference," Journal of Statistical Software, Foundation for Open Access Statistics, vol. 79(i01).
    9. G. Guindon, 2014. "The impact of tobacco prices on smoking onset in Vietnam: duration analyses of retrospective data," The European Journal of Health Economics, Springer;Deutsche Gesellschaft für Gesundheitsökonomie (DGGÖ), vol. 15(1), pages 19-39, January.
    10. Ji, Yonggang & Lin, Nan & Zhang, Baoxue, 2012. "Model selection in binary and tobit quantile regression using the Gibbs sampler," Computational Statistics & Data Analysis, Elsevier, vol. 56(4), pages 827-839.
    11. Chaton, Corinne & Lacroix, Elie, 2018. "Does France have a fuel poverty trap?," Energy Policy, Elsevier, vol. 113(C), pages 258-268.
    12. Eijffinger, Sylvester & Mahieu, Ronald & Raes, Louis, 2018. "Inferring hawks and doves from voting records," European Journal of Political Economy, Elsevier, vol. 51(C), pages 107-120.
    13. Zhang, Ping & Serban, Nicoleta, 2007. "Discovery, visualization and performance analysis of enterprise workflow," Computational Statistics & Data Analysis, Elsevier, vol. 51(5), pages 2670-2687, February.
    14. Govert E. Bijwaard, 2008. "Modeling Migration Dynamics of Immigrants," Tinbergen Institute Discussion Papers 08-070/4, Tinbergen Institute.
    15. Martin Hernani Merino & Enver Gerald Tarazona Vargas & Antonieta Hamann Pastorino & José Afonso Mazzon, 2014. "Validation of Sustainable Development Practices Scale Using the Bayesian Approach to Item Response Theory," Tržište/Market, Faculty of Economics and Business, University of Zagreb, vol. 26(2), pages 147-162.
    16. Rumyantseva, Ekaterina & Furmanov, Kirill, 2016. "Modeling mortgage survival," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 41, pages 123-143.
    17. Jurajda, Stepan, 2002. "Estimating the effect of unemployment insurance compensation on the labor market histories of displaced workers," Journal of Econometrics, Elsevier, vol. 108(2), pages 227-252, June.
    18. Emmanuel Mensaklo & Chukiat Chaiboonsri & Kanchana Chokethaworn & Songsak Sriboonchitta, 2023. "Comparing Classical and Bayesian Panel Kink Regression Frameworks in Estimating the Impact of Economic Freedom on Economic Growth," Economies, MDPI, vol. 11(10), pages 1-24, October.
    19. Daniel W. Hill Jr., 2016. "Avoiding Obligation," Journal of Conflict Resolution, Peace Science Society (International), vol. 60(6), pages 1129-1158, September.
    20. David H. Clark & Patrick M. Regan, 2003. "Opportunities to Fight," Journal of Conflict Resolution, Peace Science Society (International), vol. 47(1), pages 94-115, February.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:9:y:2021:i:6:p:639-:d:519000. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.