中国环境公益诉讼之兴起与走势——基于环境正义与环境诉讼价值进路的分析

在我国,环境污染及其造成的损害日益严重,客观上对损害救济的相关法律提出了变革与发展的要求。然而,传统环境侵权诉讼在原告资格、诉讼事由和诉讼主体——环保团体的诉讼资格方面面临诸多困境。环境公益诉讼在原告的起诉资格、诉讼标的、诉讼事由以及诉讼性质等方面,弥补了传统环境侵权诉讼的缺陷,在实现环境正义、保护环境公益、救济环境损害方面起到了不可替代的作用。     

学术注释千古事,循规尊贤始有成——对李大钊论著中四个外国人名注释及相关问题的再探讨

李大钊论著中包含大量当代读者所不熟悉的外国人名,如何把这些外国人物注释出来,帮助读者读懂李大钊的文章,是一个难度很大的课题,稍有不慎或想当然,就会造成误注,从而误导读者。《新版〈李大钊全集〉注释疏证》中对鲍德荫、司铁特、古里天森等外国人物的注释错误很典型的反映了这方面的问题。要避免类似的错误,准确注释李大钊,就必须谨守规范,辨清史实,探析研究。     

我国商业银行无形资产统计问题研究

摘　　要:商业银行无形资产是形成商业银行核心竞争力的基础和产生超额收益的源泉,加强商业银行无形资产统计工作,有利于充分揭示商业银行无形资产基本内容和对核心竞争力的影响。笔者认为,目前我国商业银行无形资产统计指标体系设置不完整,统计制度不健全,统计内容不全面,统计分析不深入,影响经济决策。笔者认为,完善商业银行无形资产统计制度时,应从组织结构类、客户关系类、知识创新类和人力资本类等四个方面对无形资产进行统计,逐步细化统计指标,明确具体的统计内容,建立、健全无形资产价值信息系统,深化无形资产统计分析工作,以此为基础,提出提高商业银行核心竞争力的基本策略。     

Ⅱ—32A的配对提纯及其纯系的性状表现

介绍了水稻不育系Ⅱ—32A的提纯经过、方法以及纯系Ⅱ—32—5A、Ⅱ—32—8A的品系纯度不育株率、不育度和生育特性等性状表现。     

Estimation with right‐censored observations under a semi‐Markov model

The semi‐Markov process often provides a better framework than the classical Markov process for the analysis of events with multiple states. The purpose of this paper is twofold. First, we show that in the presence of right censoring, when the right end‐point of the support of the censoring time is strictly less than the right end‐point of the support of the semi‐Markov kernel, the transition probability of the semi‐Markov process is nonidentifiable, and the estimators proposed in the literature are inconsistent in general. We derive the set of all attainable values for the transition probability based on the censored data, and we propose a nonparametric inference procedure for the transition probability using this set. Second, the conventional approach to constructing confidence bands is not applicable for the semi‐Markov kernel and the sojourn time distribution. We propose new perturbation resampling methods to construct these confidence bands. Different weights and transformations are explored in the construction. We use simulation to examine our proposals and illustrate them with hospitalization data from a recent cancer survivor study. The Canadian Journal of Statistics 41: 237–256; 2013 © 2013 Statistical Society of Canada     

Asymptotic normality of Huber–Dutter estimators in a linear model with AR(1) processes

The paper studies a linear regression model with first order autoregressive (AR(1)) processes. The Huber–Dutter (HD) estimators of unknown parameters are given, and the asymptotic normality of the HD estimators is investigated. An example is presented to illustrate the proposed method.     

Bayesian quantile regression for single-index models

Using an asymmetric Laplace distribution, which provides a mechanism for Bayesian inference of quantile regression models, we develop a fully Bayesian approach to fitting single-index models in conditional quantile regression. In this work, we use a Gaussian process prior for the unknown nonparametric link function and a Laplace distribution on the index vector, with the latter motivated by the recent popularity of the Bayesian lasso idea. We design a Markov chain Monte Carlo algorithm for posterior inference. Careful consideration of the singularity of the kernel matrix, and tractability of some of the full conditional distributions leads to a partially collapsed approach where the nonparametric link function is integrated out in some of the sampling steps. Our simulations demonstrate the superior performance of the Bayesian method versus the frequentist approach. The method is further illustrated by an application to the hurricane data.     

Complete convergence for arrays of rowwise negatively superadditive-dependent random variables and its applications

In this paper, the Rosenthal-type maximal inequalities and Kolmogorov-type exponential inequality for negatively superadditive-dependent (NSD) random variables are presented. By using these inequalities, we study the complete convergence for arrays of rowwise NSD random variables. As applications, the Baum–Katz-type result for arrays of rowwise NSD random variables and the complete consistency for the estimator of nonparametric regression model based on NSD errors are obtained. Our results extend and improve the corresponding ones of Chen et al. [On complete convergence for arrays of rowwise negatively associated random variables. Theory Probab Appl. 2007;52(2):393–397] for arrays of rowwise negatively associated random variables to the case of arrays of rowwise NSD random variables.     

Statistical inference on asymptotic properties of two estimators for the partially linear single-index models

The outer product of gradients (OPG) estimation procedure based on least squares (LS) approach has been presented by Xia et al. [An adaptive estimation of dimension reduction space. J Roy Statist Soc Ser B. 2002;64:363–410] to estimate the single-index parameter in partially linear single-index models (PLSIM). However, its asymptotic property has not been established yet and the efficiency of LS-based method can be significantly affected by outliers and heavy-tailed distributions. In this paper, we firstly derive the asymptotic property of OPG estimator developed by Xia et al. [An adaptive estimation of dimension reduction space. J Roy Statist Soc Ser B. 2002;64:363–410] in theory, and a novel robust estimation procedure combining the ideas of OPG and local rank (LR) inference is further developed for PLSIM along with its theoretical property. Then, we theoretically derive the asymptotic relative efficiency (ARE) of the proposed LR-based procedure with respect to LS-based method, which is shown to possess an expression that is closely related to that of the signed-rank Wilcoxon test in comparison with the t-test. Moreover, we demonstrate that the new proposed estimator has a great efficiency gain across a wide spectrum of non-normal error distributions and almost not lose any efficiency for the normal error. Even in the worst case scenarios, the ARE owns a lower bound equalling to 0.864 for estimating the single-index parameter and a lower bound being 0.8896 for estimating the nonparametric function respectively, versus the LS-based estimators. Finally, some Monte Carlo simulations and a real data analysis are conducted to illustrate the finite sample performance of the estimators.