An Alternative Approach to Modeling Recidivism Using Quantile Residual Life Functions

Raymond Ellermann, Pasquale Sullo and James M. Tien
Additional contact information
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

Operations Research, 1992, vol. 40, issue 3, 485-504

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.

Keywords: judicial/legal; crime: recidivism modeling; analysis of data; probability: mixture models and Burr distributions; statistics: data analysis; quantile residual life function plotting (search for similar items in EconPapers)
Date: 1992
References: Add references at CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
http://dx.doi.org/10.1287/opre.40.3.485 (application/pdf)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:inm:oropre:v:40:y:1992:i:3:p:485-504

Access Statistics for this article

More articles in Operations Research from INFORMS Contact information at EDIRC.
Bibliographic data for series maintained by Chris Asher ().