Journal of Data Science

Addressing short-term recidivism presents a significant challenge within the criminal justice system, necessitating robust predictive models that can accurately identify risk factors and predict outcomes. This paper introduces application of Bayesian cure rate models to predict short-term recidivism using data from individuals released from Iowa prisons in 2018. The mixture cure rate models effectively capture the recidivism risk by distinguishing between cured individuals, those unlikely to re-offend, and those susceptible to relapse. By implementing accelerated failure time models combined with logistic regression and various link functions, including logit, skewed logit, reversed power logit, and flexible generalized logit, we enhance the model’s flexibility and interpretability. Through a comparative analysis of these models, key predictors of recidivism are identified and discussed in detail.