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An Alternative Approach to Modeling Recidivism Using Quantile Residual Life Functions

Author

Listed:
  • Raymond Ellermann

    (Office of Mental Health, State of New York)

  • Pasquale Sullo

    (Rensselaer Polytechnic Institute, Troy, New York)

  • James M. Tien

    (Rensselaer Polytechnic Institute, Troy, New York)

Abstract

Empirical estimates of quantile residual life functions can be employed effectively to obtain properties of recidivism and to help screen parametric mixture models. In this manner, the Burr model is demonstrated to be an appropriate model for characterizing recidivism. When applied to certain data, the model suggests that while the observed declining recidivism rate can be explained by population heterogeneity, individual recidivism rates may in fact be increasing. The quantile residual life function approach to modeling recidivism is applied to two often-referenced data sets, as well as to an extensive data set obtained from the State of New York which is new to the criminal justice literature.

Suggested Citation

  • Raymond Ellermann & Pasquale Sullo & James M. Tien, 1992. "An Alternative Approach to Modeling Recidivism Using Quantile Residual Life Functions," Operations Research, INFORMS, vol. 40(3), pages 485-504, June.
    • Handle: RePEc:inm:oropre:v:40:y:1992:i:3:p:485-504
    DOI: 10.1287/opre.40.3.485
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    Cited by:

    1. 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.

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