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Learning from multiple analogies: an Information Theoretic framework for predicting criminal recidivism

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  • Bhati, Avinash

Abstract

If 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.

Suggested Citation

  • Bhati, Avinash, 2007. "Learning from multiple analogies: an Information Theoretic framework for predicting criminal recidivism," MPRA Paper 11850, University Library of Munich, Germany.
    • Handle: RePEc:pra:mprapa:11850
    as

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    File URL: https://mpra.ub.uni-muenchen.de/11850/1/MPRA_paper_11850.pdf
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    References listed on IDEAS

    as
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    More about this item

    Keywords

    information theory; criminal recidivism; predictive modeling; multiple analogies;
    All these keywords.

    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

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