Predicting Recidivism Using Survival Models

  • Peter Schmidt
  • Ann Dryden Witte

Part of the Research in Criminology book series (RESEARCH CRIM.)

Table of contents

  1. Front Matter
    Pages i-xi
  2. Peter Schmidt, Ann Dryden Witte
    Pages 1-20
  3. Peter Schmidt, Ann Dryden Witte
    Pages 21-33
  4. Peter Schmidt, Ann Dryden Witte
    Pages 34-47
  5. Peter Schmidt, Ann Dryden Witte
    Pages 48-65
  6. Peter Schmidt, Ann Dryden Witte
    Pages 66-82
  7. Peter Schmidt, Ann Dryden Witte
    Pages 83-90
  8. Peter Schmidt, Ann Dryden Witte
    Pages 91-118
  9. Peter Schmidt, Ann Dryden Witte
    Pages 119-150
  10. Peter Schmidt, Ann Dryden Witte
    Pages 151-160
  11. Back Matter
    Pages 161-174

About this book

Introduction

Our interest in the statistical modeling of data on the timing of recidivism began in the mid 1970s when we were both junior members of the eco­ nomics department at the University of North Carolina. At that time, methods of analyzing qualitative and limited variables were being developed rapidly in the econometric literature, and we became interested in finding a suitable application for these new methods. Data on the timing of recidivism offered unique and interesting statistical challenges, such as skewness of the distribution and the presence of censoring. Being young and foolish, we decided it would be fun to try something "really" difficult. And, being young and ignorant, we were blissfully unaware of the con­ current developments in the statistical modeling of survival times that were then appearing in the biostatistics, operations research, and criminological literatures. In the course of some earlier research, we had learned that the North Carolina Department of Correction had an unusually well-developed data base on their inmates. We approached the Department and asked if they would be interested in working with us to develop models that would predict when their former charges would return to their custody. They agreed because they were interested in using such models to evaluate rehabilitative programs and alternative prison management systems and to help project future prison populations.

Keywords

Censoring Recidivism statistical method statistics

Authors and affiliations

  • Peter Schmidt
    • 1
  • Ann Dryden Witte
    • 2
    • 3
  1. 1.Department of EconomicsMichigan State UniversityUSA
  2. 2.Department of EconomicsWellesley CollegeUSA
  3. 3.Research AssociateNational Bureau of Economic ResearchCambridgeUSA

Bibliographic information

  • DOI https://doi.org/10.1007/978-1-4612-3772-3
  • Copyright Information Springer-Verlag New York 1988
  • Publisher Name Springer, New York, NY
  • eBook Packages Springer Book Archive
  • Print ISBN 978-1-4612-8343-0
  • Online ISBN 978-1-4612-3772-3
  • Series Print ISSN 1431-7540
  • About this book