Changing approaches of prosecutors towards juvenile repeated sex-offenders: A Bayesian evaluation

Dipankar Bandyopadhyay, Debajyoti Sinha, Stuart Lipsitz, Elizabeth Letourneau

Research output: Contribution to journalArticlepeer-review

2 Scopus citations


Existing state-wide data bases on prosecutors' decisions about juvenile offenders are important, yet often un-explored resources for understanding changes in patterns of judicial decisions over time. We investigate the extent and nature of change in judicial behavior toward juveniles following the enactment of a new set of mandatory registration policies between 1992 and 1996 via analyzing the data on prosecutors' decisions of moving forward for youths repeatedly charged with sexual violence in South Carolina. To analyze this longitudinal binary data, we use a random effects logistic regression model via incorporating an unknown change-point year. For convenient physical interpretation, our models allow the proportional odds interpretation of effects of the explanatory variables and the change-point year with and without conditioning on the youth-specific random effects. As a consequence, the effects of the unknown change-point year and other factors can be interpreted as changes in both within youth and population averaged odds of moving forward. Using a Bayesian paradigm, we consider various prior opinions about the unknown year of the change in the pattern of prosecutors' decision. Based on the available data, we make posteriori conclusions about whether a change-point has occurred between 1992 and 1996 (inclusive), evaluate the degree of confidence about the year of change-point, estimate the magnitude of the effects of the change-point and other factors, and investigate other provocative questions about patterns of prosecutors' decisions over time.

Original languageEnglish (US)
Pages (from-to)805-829
Number of pages25
JournalAnnals of Applied Statistics
Issue number2
StatePublished - Jun 2010
Externally publishedYes


  • Bridge density
  • Change-point
  • Dirichlet prior
  • Markov chain Monte Carlo

ASJC Scopus subject areas

  • Statistics and Probability
  • Modeling and Simulation
  • Statistics, Probability and Uncertainty


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