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Examining deterrence of adult sex crimes: A semi-parametric intervention time-series approach

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  • Park, Jin-Hong
  • Bandyopadhyay, Dipankar
  • Letourneau, Elizabeth

Abstract

Motivated by recent developments on dimension reduction (DR) techniques for time-series data, the association of a general deterrent effect with the registration and notification (SORN) policy of South Carolina (SC) for preventing sex crimes was examined. Using adult sex crime arrestee data from 1990 to 2005, the idea of a central mean subspace (CMS) is extended to intervention time-series analysis (CMS-ITS) to model the sequential intervention effects of 1995 (the year SC’s SORN policy was initially implemented) and 1999 (the year the policy was revised to include online notification) on the time-series spectrum. The CMS-ITS model estimation was achieved via kernel smoothing techniques, and compared to interrupted auto-regressive integrated time-series (ARIMA) models. Simulation studies and application to the real data underscore our model’s ability of achieving parsimony, and of detecting intervention effects not earlier determined via traditional ARIMA models. From a public health perspective, findings from this study draw attention to the potential general deterrent effects of SC’s SORN policy. These findings are considered in light of the overall body of research on sex crime arrestee registration and notification policies, which remain controversial.

Suggested Citation

  • Park, Jin-Hong & Bandyopadhyay, Dipankar & Letourneau, Elizabeth, 2014. "Examining deterrence of adult sex crimes: A semi-parametric intervention time-series approach," Computational Statistics & Data Analysis, Elsevier, vol. 69(C), pages 198-207.
    • Handle: RePEc:eee:csdana:v:69:y:2014:i:c:p:198-207
    DOI: 10.1016/j.csda.2013.08.004
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    1. Ye Z. & Weiss R.E., 2003. "Using the Bootstrap to Select One of a New Class of Dimension Reduction Methods," Journal of the American Statistical Association, American Statistical Association, vol. 98, pages 968-979, January.
    2. Cai, Zongwu & Fan, Jianqing & Yao, Qiwei, 2000. "Functional-coefficient regression models for nonlinear time series," LSE Research Online Documents on Economics 6314, London School of Economics and Political Science, LSE Library.
    3. Wolfgang Härdle & Helmut Lütkepohl & Rong Chen, 1997. "A Review of Nonparametric Time Series Analysis," International Statistical Review, International Statistical Institute, vol. 65(1), pages 49-72, April.
    4. Yingcun Xia & Howell Tong & W. K. Li & Li‐Xing Zhu, 2002. "An adaptive estimation of dimension reduction space," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 64(3), pages 363-410, August.
    5. Isaac Ehrlich, 1996. "Crime, Punishment, and the Market for Offenses," Journal of Economic Perspectives, American Economic Association, vol. 10(1), pages 43-67, Winter.
    6. Yin, Xiangrong & Li, Bing & Cook, R. Dennis, 2008. "Successive direction extraction for estimating the central subspace in a multiple-index regression," Journal of Multivariate Analysis, Elsevier, vol. 99(8), pages 1733-1757, September.
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