Learning from multiple analogies: an Information Theoretic framework for predicting criminal recidivism
AbstractIf recidivism is defined as rearrest within a finite period following release from prison, then the kinds of outcomes typically available to researchers include: (i) whether or not the individual was rearrested within the follow-up period; (ii) how many times the individual was rearrested; and (iii) what was the duration from release to first (or subsequent) rearrest. Since these outcomes are all different manifestations of the same underlying stochastic process, they provide multiple analogies from which to recover information about it. This paper develops a semi-parametric approach for utilizing information in these, and several other related outcomes, to predict criminal recidivism and presents preliminary findings.
Download InfoIf you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
Bibliographic InfoPaper provided by University Library of Munich, Germany in its series MPRA Paper with number 11850.
Date of creation: 2007
Date of revision:
information theory; criminal recidivism; predictive modeling; multiple analogies;
Find related papers by JEL classification:
- C3 - Mathematical and Quantitative Methods - - Multiple or Simultaneous Equation Models; Multiple Variables
- C4 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods: Special Topics
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Zellner, A., 1988. "Optimal Information-Processing And Bayes' Theorem," Papers m8803, Southern California - Department of Economics.
- Ryu, Hang K., 1993. "Maximum entropy estimation of density and regression functions," Journal of Econometrics, Elsevier, vol. 56(3), pages 397-440, April.
- Heckman, James J. & Singer, Burton, 1984. "Econometric duration analysis," Journal of Econometrics, Elsevier, vol. 24(1-2), pages 63-132.
- Winkelmann, Rainer, 1995. "Duration Dependence and Dispersion in Count-Data Models," Journal of Business & Economic Statistics, American Statistical Association, vol. 13(4), pages 467-74, October.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Ekkehart Schlicht).
If references are entirely missing, you can add them using this form.