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 towards South Carolina (SC)'s registration and notification (SORN) policy for preventing sex crimes was examined. Using adult sex crime arrestee data from 1990 to 2005, the the idea of 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 underscores our model's ability towards achieving parsimony, and to detect 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.
Year
DOI
Venue
2014
10.1016/j.csda.2013.08.004
Computational Statistics & Data Analysis
Keywords
Field
DocType
sex crime arrestee,adult sex crime arrestee,online notification,intervention effect,interrupted auto-regressive integrated time-series,central mean subspace,intervention time-series analysis,time-series data,nadaraya-watson kernel smoother,notification policy,cms-its model estimation,sorn policy,nonlinear time series,semi-parametric intervention time-series approach,time-series spectrum,examining deterrence,biomedical research,bioinformatics
Public health,Econometrics,Deterrence theory,Kernel smoother,Computer science,Autoregressive integrated moving average,Semiparametric model,Statistics
Journal
Volume
ISSN
Citations 
69
0167-9473
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
Jin-Hong Park100.34
Dipankar Bandyopadhyay201.35
Elizabeth Letourneau300.34