财政分权和环境规制促进了中国绿色技术创新吗？

基于我国2003-2017年省级面板数据测算财政分权水平和财政分权效率,结合两阶段关联网络DEA测算绿色创新效率、绿色技术研发效率、绿色技术成果转化效率。结合理论模型与实证模型,分析了财政分权、环境规制对我国绿色技术创新的影响。结果表明：从整体上看,财政分权水平、财政分权效率和环境规制都是推动我国绿色创新发展的积极因素,财政分权还通过影响环境规制对绿色技术创新产生积极间接影响;但也导致了地方政府不注重绿色研发的短视行为。从不同时段看,财政分权和环境规制对绿色创新影响发生了变化,一是国际金融危机以后,财政分权加大了经济刺激政策对绿色创新发展的挤出效应,但这在新时代以后得到缓解;二是2013年以后地方环境规制水平提升过快,在一定程度上反而不利于绿色创新。从发展处于不同分位的地区看,财政分权对高、低分位地区绿色创新发展支持都稍显不足,但提高财政分权效率则能显著带动高分位地区发展;环境规制只在高分位地区促进了绿色创新发展,但对绿色创新效率较低的地区而言,过高的环境规制并没有实际意义。     

新传媒,新世界: 网络时代的哲学反思

新传媒正在从时空观念、经济组织形式和社会结构等方面全方位地形成一个新世界,这个新世界是一个以互联网为社会深层结构的开放世界,它的地平线已经显现。作为新世界的网络世界,有其自身的逻辑,但离不开人类的积极参与。新世界在方法论上,更倾向于归纳逻辑、认知逻辑和论辩理论。新世界的出现使人们对资本主义何时灭亡、人的主体地位等问题产生更多的开放性思考。     

Inference in Semi‐Parametric Dynamic Models for Repeated Count Data

This paper deals with a longitudinal semi‐parametric regression model in a generalised linear model setup for repeated count data collected from a large number of independent individuals. To accommodate the longitudinal correlations, we consider a dynamic model for repeated counts which has decaying auto‐correlations as the time lag increases between the repeated responses. The semi‐parametric regression function involved in the model contains a specified regression function in some suitable time‐dependent covariates and a non‐parametric function in some other time‐dependent covariates. As far as the inference is concerned, because the non‐parametric function is of secondary interest, we estimate this function consistently using the independence assumption‐based well‐known quasi‐likelihood approach. Next, the proposed longitudinal correlation structure and the estimate of the non‐parametric function are used to develop a semi‐parametric generalised quasi‐likelihood approach for consistent and efficient estimation of the regression effects in the parametric regression function. The finite sample performance of the proposed estimation approach is examined through an intensive simulation study based on both large and small samples. Both balanced and unbalanced cluster sizes are incorporated in the simulation study. The asymptotic performances of the estimators are given. The estimation methodology is illustrated by reanalysing the well‐known health care utilisation data consisting of counts of yearly visits to a physician by 180 individuals for four years and several important primary and secondary covariates.     

Empirical likelihood based detection procedure for change point in mean residual life functions under random censorship

The mean residual life (MRL) function is one of the basic parameters of interest in survival analysis that describes the expected remaining time of an individual after a certain age. The study of changes in the MRL function is practical and interesting because it may help us to identify some factors such as age and gender that may influence the remaining lifetimes of patients after receiving a certain surgery. In this paper, we propose a detection procedure based on the empirical likelihood for the changes in MRL functions with right censored data. Two real examples are also given: Veterans' administration lung cancer study and Stanford heart transplant to illustrate the detecting procedure. Copyright © 2016 John Wiley & Sons, Ltd.     

Mixed-effects Models with Skewed Distributions for Time-varying Decay Rate in HIV Dynamics

After initiation of treatment, HIV viral load has multiphasic changes, which indicates that the viral decay rate is a time-varying process. Mixed-effects models with different time-varying decay rate functions have been proposed in literature. However, there are two unresolved critical issues: (i) it is not clear which model is more appropriate for practical use, and (ii) the model random errors are commonly assumed to follow a normal distribution, which may be unrealistic and can obscure important features of within- and among-subject variations. Because asymmetry of HIV viral load data is still noticeable even after transformation, it is important to use a more general distribution family that enables the unrealistic normal assumption to be relaxed. We developed skew-elliptical (SE) Bayesian mixed-effects models by considering the model random errors to have an SE distribution. We compared the performance among five SE models that have different time-varying decay rate functions. For each model, we also contrasted the performance under different model random error assumptions such as normal, Student-t, skew-normal, or skew-t distribution. Two AIDS clinical trial datasets were used to illustrate the proposed models and methods. The results indicate that the model with a time-varying viral decay rate that has two exponential components is preferred. Among the four distribution assumptions, the skew-t and skew-normal models provided better fitting to the data than normal or Student-t model, suggesting that it is important to assume a model with a skewed distribution in order to achieve reasonable results when the data exhibit skewness.     

Power analysis for clustered non-continuous responses in multicenter trials

Power analysis for multi-center randomized control trials is quite difficult to perform for non-continuous responses when site differences are modeled by random effects using the generalized linear mixed-effects model (GLMM). First, it is not possible to construct power functions analytically, because of the extreme complexity of the sampling distribution of parameter estimates. Second, Monte Carlo (MC) simulation, a popular option for estimating power for complex models, does not work within the current context because of a lack of methods and software packages that would provide reliable estimates for fitting such GLMMs. For example, even statistical packages from software giants like SAS do not provide reliable estimates at the time of writing. Another major limitation of MC simulation is the lengthy running time, especially for complex models such as GLMM, especially when estimating power for multiple scenarios of interest. We present a new approach to address such limitations. The proposed approach defines a marginal model to approximate the GLMM and estimates power without relying on MC simulation. The approach is illustrated with both real and simulated data, with the simulation study demonstrating good performance of the method.     

Stochastic comparisons on sample extremes of dependent and heterogenous observations

Stochastic comparison on order statistics from heterogeneous-dependent observations has been paid lots of attention recently. This paper devotes to investigating the ordering properties of order statistics from dependent observations. We derive the usual stochastic order for sample minimums and the second smallest order statistic, the dispersive order and the star order for minimums of samples having proportional hazards and Archimedean survival copulas. Similar ordering results are also obtained for maximums and the second largest order statistic of samples having proportional reversed hazards and Archimedean copulas. Several examples illustrating the main results are presented as well.     

Functional Partial Linear Single‐index Model

This paper deals with the problem of predicting the real‐valued response variable using explanatory variables containing both multivariate random variable and random curve. The proposed functional partial linear single‐index model treats the multivariate random variable as linear part and the random curve as functional single‐index part, respectively. To estimate the non‐parametric link function, the functional single‐index and the parameters in the linear part, a two‐stage estimation procedure is proposed. Compared with existing semi‐parametric methods, the proposed approach requires no initial estimation and iteration. Asymptotical properties are established for both the parameters in the linear part and the functional single‐index. The convergence rate for the non‐parametric link function is also given. In addition, asymptotical normality of the error variance is obtained that facilitates the construction of confidence region and hypothesis testing for the unknown parameter. Numerical experiments including simulation studies and a real‐data analysis are conducted to evaluate the empirical performance of the proposed method.     

Prevalence and characteristics of sexual violence victimization among U.S. adults, 2001-2003

This article provides the most recent U.S. prevalence estimates of forced sex and unwanted sexual activity. Results of a national telephone survey conducted in 2001-2003 indicate that 1 in 59 U.S. adults (2.7 million women and 978,000 men) experienced unwanted sexual activity in the 12 months preceding the survey and that 1 in 15 U.S. adults (11.7 million women and 2.1 million men) have been forced to have sex during their lifetime. There were 60.4% of females and 69.2% of males who were 17 years old or younger at the time the first forced sex occurred. This study provides an update to the National Violence Against Women Survey with more recent national data. Findings suggest that victimization rates have remained consistent since the 1990s. These findings suggest that a continued effort toward primary prevention of sexual violence, particularly rape of children and adolescents, is needed.