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.
authors.affiliation: Department of Mathematics, College of Charleston, Charleston, SC, USA.
authors.forename: Jin-Hong
authors.initials: JH
authors.lastname: Park
authors.affiliation: Division of Biostatistics, University of Minnesota, Minneapolis, MN, USA.
authors.forename: Dipankar
authors.initials: D
authors.lastname: Bandyopadhyay
authors.affiliation: Department of Mental Health, Johns Hopkins University, Baltimore, MD, USA.
authors.forename: Elizabeth
authors.initials: E
authors.lastname: Letourneau
keywords: Central mean subspace; Nadaraya-Watson kernel smoother; Nonlinear time series; Sex crime arrestee