“功能性表达性模型”与中国警察信任——基于全国样本的多重中介与调节模型分析

功能性模型认为，提升警察信任的主要方法在于预防和控制犯罪，降低犯罪率与提升公众安全感；而表达性模型认为，提升警察信任更应注重维护社会正义与社区凝聚力。基于提出促进中国警察信任度提高的理论模型，提升公众获得感、幸福感、安全感，实现国家和社会整体安全的最终目的，采用结构方程模型验证“功能性-表达性模型”与中国警察信任关系的结果表明，功能性模型与表达性模型内部因素之间存在显著路径关系，同时功能性模型显著影响表达性模型指标进而改变公众对警察的信任程度。经调节效应分析后发现，主观社会阶层负向调节被害经历到犯罪恐惧感的直接路径，社交媒体使用负向调节犯罪恐惧感到警察信任的直接路径；在主观社会阶层和社交媒体使用的共同调节作用下，“被害经历→犯罪恐惧感→警察信任”中介路径效应值，随社交媒体使用频率的提升而增大，随主观社会阶层的升高而减小。     

An Evaluation of Mechanisms of Change in Multisystemic Therapy for Juvenile Justice‐Involved Youths A Decade Following Treatment

Although researchers have identified the more immediate mechanisms of change in family‐based treatments for juvenile justice‐involved youths, it is not known whether these same mechanisms continue to prevent criminal offending into adulthood. The present study evaluated whether caregiver‐directed improvements in family relations, youth prosocial peer relations, and youth academic performance during multisystemic therapy (MST) for serious and violent juvenile offenders had an impact on young adult involvement in criminal activity and sentencing 10.2 years following treatment. The results showed that improvements in family relations were associated with reduced odds of criminal outcomes a decade later for former MST participants. Furthermore, improvements in youth prosocial peer relations and academic performance were also related to lower odds of long‐term criminal activity. These results are consistent with the underlying theory of change in family‐based treatments and demonstrate that caregivers are critical to achieving and sustaining decreased antisocial behavior for youths with serious and violent criminal histories.     

Exploring the Effects of Conditioning Factors on the Strain–Crime Association: A Test on Incarcerated Chinese Women

A key assumption of general strain theory (GST) is that various factors condition the effects of strains on crime. Past research examining this conditioning hypothesis tended to focus on youth samples and use gender as a control variable. Using survey data from Chinese female inmates, this study tests the strain–crime relationship posited in GST as well as the hypothesized effects of the conditioning factors. Regression results show that different types of strains have distinct effects on respondents’ odds to commit violent over property crimes. Conditioning factors are found to moderate the strengths of strains toward respondents’ likelihood to commit property crimes over violent crimes.     

Correlation properties of continuous-time autoregressive processes delayed by the inverse of the stable subordinator

Abstract

We define the delayed Lévy-driven continuous-time autoregressive process via the inverse of the stable subordinator. We derive correlation structure for the observed non-stationary delayed Lévy-driven continuous-time autoregressive processes of order p, emphasizing low orders, and we show they exhibit long-range dependence property. Distributional properties are discussed as well.     

Beta-Bernstein Smoothing for Regression Curves with Compact Support

ABSTRACT. The problem of boundary bias is associated with kernel estimation for regression curves with compact support. This paper proposes a simple and uni(r)ed approach for remedying boundary bias in non-parametric regression, without dividing the compact support into interior and boundary areas and without applying explicitly different smoothing treatments separately. The approach uses the beta family of density functions as kernels. The shapes of the kernels vary according to the position where the curve estimate is made. Theyare symmetric at the middle of the support interval, and become more and more asymmetric nearer the boundary points. The kernels never put any weight outside the data support interval, and thus avoid boundary bias. The method is a generalization of classical Bernstein polynomials, one of the earliest methods of statistical smoothing. The proposed estimator has optimal mean integrated squared error at an order of magnitude n ^−4/5, equivalent to that of standard kernel estimators when the curve has an unbounded support.