论社区矫正中矫正地变更的若干问题

我国以社区矫正人员的居住地为矫正地。矫正地变更主要包括迁居型、暂离型、外出型三种类型。迁居型矫正地变更后,由新矫正地的社区矫正机构进行矫正;暂离型矫正地变更后,由现居住地矫正机构委托异地暂时居住地社区矫正机构进行矫正;外出型矫正地变更须由现矫正地社区矫正机构进行延伸矫正,并强化矫正措施。     

流散与文化认同——《奥斯卡·瓦奥短暂而奇妙的一生》评析

多米尼加裔美国作家朱诺·迪亚斯的小说《奥斯卡·瓦奥短暂而奇妙的一生》以多米尼加共和国遭受的殖民侵略与独裁统治为背景,描写了多米尼加流散群体受到的历史与现实的创伤。小说中过去与现在交替、人物的"缺失"与"再现"并存,不仅重塑了多米尼加共和国历史以及奥斯卡家族历史,还展现了流散者在美国遭遇的文化冲突与边缘化境遇。种族、阶级与文化差异以及无法逾越的他者身份造成流散者的文化归属与认同陷入困境,而多元的文化背景使杂交性的文化认同成为渴望寻找自己文化定位的流散者的一种生存选择。     

A nonparametric hypothesis test for heteroscedasticity

In this paper, a hypothesis test for heteroscedasticity is proposed in a nonparametric regression model. The test statistic, which uses the residuals from a nonparametric fit of the mean function, is based on an adaptation of the well-known Levene's test. Using the recent theory for analysis of variance when the number of factor levels goes to infinity, the asymptotic distribution of the test statistic is established under the null hypothesis of homocedasticity and under local alternatives. Simulations suggest that the proposed test performs well in several situations, especially when the variance is a nonlinear function of the predictor.     

Subsampling-extrapolation bandwidth selection in bivariate kernel density estimation

This paper focuses on bivariate kernel density estimation that bridges the gap between univariate and multivariate applications. We propose a subsampling-extrapolation bandwidth matrix selector that improves the reliability of the conventional cross-validation method. The proposed procedure combines a U-statistic expression of the mean integrated squared error and asymptotic theory, and can be used in both cases of diagonal bandwidth matrix and unconstrained bandwidth matrix. In the subsampling stage, one takes advantage of the reduced variability of estimating the bandwidth matrix at a smaller subsample size m (m < n); in the extrapolation stage, a simple linear extrapolation is used to remove the incurred bias. Simulation studies reveal that the proposed method reduces the variability of the cross-validation method by about 50% and achieves an expected integrated squared error that is up to 30% smaller than that of the benchmark cross-validation. It shows comparable or improved performance compared to other competitors across six distributions in terms of the expected integrated squared error. We prove that the components of the selected bivariate bandwidth matrix have an asymptotic multivariate normal distribution, and also present the relative rate of convergence of the proposed bandwidth selector.     

Prediction of future failures times based on Type-I hybrid censored samples of random sample sizes

In this article, we consider the prediction of future failure times based on Type-I hybrid censored samples. Point predictors and prediction intervals using different procedures are discussed for a general model. The exponential and Rayleigh distributions are used as illustrative examples to show the most simplified forms of the so obtained predictors as well as prediction intervals. Intensive simulation study and a real life dataset are presented to illustrate our findings and results.     

How Many More Missing Women? Excess Female Mortality and Prenatal Sex Selection, 1970–2050

Sex‐based discrimination has resulted in severe demographic imbalances between males and females, culminating in a large number of “missing women” in several countries around the world. We provide new estimates and projections of the number of missing females and of the roles played by prenatal and postnatal factors in this imbalance. We estimate time series of the number of missing females, the number of excess female deaths, and the number of missing female births for the world and selected countries. Estimates are provided for 1970–2010 and projections are made from 2010 to 2050. We show that the estimates of these different indicators are consistent with one another and account for the dynamics of the population of missing females over time. We conclude that the number of missing females has steadily risen in the past decades, reaching 126 million in 2010, and the number is expected to peak at 150 million in 2035. Excess mortality was the dominant cause of missing females in the past, and this is expected to remain the case in future decades in spite of the recent rise of prenatal sex selection. The annual number of newly missing females reached 3.4 million in 2010 and is expected to remain above 3 million every year until 2050.     

Local influence in seemingly unrelated regression model with ridge estimate

Local influence is a well-known method for identifying the influential observations in a dataset and commonly needed in a statistical analysis. In this paper, we study the local influence on the parameters of interest in the seemingly unrelated regression model with ridge estimation, when there exists collinearity among the explanatory variables. We examine two types of perturbation schemes to identify influential observations: the perturbation of variance and the perturbation of individual explanatory variables. Finally, the efficacy of our proposed method is illustrated by analyzing [13 A. Munnell, Why has productivity declined? Productivity and public investment, New Engl. Econ. Rev. (1990), pp. 3–22. [Google Scholar]] productivity dataset.     

A new family of multivariate slash distributions

In this article, a new form of multivariate slash distribution is introduced and some statistical properties are derived. In order to illustrate the advantage of this distribution over the existing generalized multivariate slash distribution in the literature, it is applied to a real data set.     

Estimation of reliability of multicomponent stress–strength for a Kumaraswamy distribution

This article deals with the Bayesian and non Bayesian estimation of multicomponent stress–strength reliability by assuming the Kumaraswamy distribution. Both stress and strength are assumed to have a Kumaraswamy distribution with common and known shape parameter. The reliability of such a system is obtained by the methods of maximum likelihood and Bayesian approach and the results are compared using Markov Chain Monte Carlo (MCMC) technique for both small and large samples. Finally, two data sets are analyzed for illustrative purposes.