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

Jin Hong Park, Dipankar Bandyopadhyay, Elizabeth J Letourneau

Research output: Contribution to journalArticle

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.

Original languageEnglish (US)
Pages (from-to)198-207
Number of pages10
JournalComputational Statistics and Data Analysis
Volume69
DOIs
StatePublished - 2014

Fingerprint

Crime
Time series
ARIMA Models
Subspace
Registration
Kernel Smoothing
Parsimony
Smoothing Techniques
Time series analysis
Time Series Analysis
Public Health
Public health
Dimension Reduction
Time Series Models
Time Series Data
Policy
Association reactions
Simulation Study
Model

Keywords

  • Central mean subspace
  • Nadaraya-Watson kernel smoother
  • Nonlinear time series
  • Sex crime arrestee

ASJC Scopus subject areas

  • Computational Mathematics
  • Computational Theory and Mathematics
  • Statistics and Probability
  • Applied Mathematics

Cite this

Examining deterrence of adult sex crimes : A semi-parametric intervention time-series approach. / Park, Jin Hong; Bandyopadhyay, Dipankar; Letourneau, Elizabeth J.

In: Computational Statistics and Data Analysis, Vol. 69, 2014, p. 198-207.

Research output: Contribution to journalArticle

@article{daad30ad34ae43c2a6e50f305d7261e2,
title = "Examining deterrence of adult sex crimes: A semi-parametric intervention time-series approach",
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.",
keywords = "Central mean subspace, Nadaraya-Watson kernel smoother, Nonlinear time series, Sex crime arrestee",
author = "Park, {Jin Hong} and Dipankar Bandyopadhyay and Letourneau, {Elizabeth J}",
year = "2014",
doi = "10.1016/j.csda.2013.08.004",
language = "English (US)",
volume = "69",
pages = "198--207",
journal = "Computational Statistics and Data Analysis",
issn = "0167-9473",
publisher = "Elsevier",

}

TY - JOUR

T1 - Examining deterrence of adult sex crimes

T2 - A semi-parametric intervention time-series approach

AU - Park, Jin Hong

AU - Bandyopadhyay, Dipankar

AU - Letourneau, Elizabeth J

PY - 2014

Y1 - 2014

N2 - 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.

AB - 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.

KW - Central mean subspace

KW - Nadaraya-Watson kernel smoother

KW - Nonlinear time series

KW - Sex crime arrestee

UR - http://www.scopus.com/inward/record.url?scp=84883735455&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84883735455&partnerID=8YFLogxK

U2 - 10.1016/j.csda.2013.08.004

DO - 10.1016/j.csda.2013.08.004

M3 - Article

C2 - 24795489

AN - SCOPUS:84883735455

VL - 69

SP - 198

EP - 207

JO - Computational Statistics and Data Analysis

JF - Computational Statistics and Data Analysis

SN - 0167-9473

ER -