生态金融的类型及其法律问题

作为环境危机的应对机制,生态金融市场因应而生。生态金融市场包括市场化机制和生态金融机制,前者以排污权交易为代表,后者的类型较为多样,比如环境基金、气候变化衍生品、自然灾害证券和生态期权等生态机制。作为新型的金融市场机制,生态金融机制对现行金融法制构成了一定的挑战。为了推动生态金融机制的发展,我国应当完善现行的金融法制,为生态金融法制提供良好的法制环境。     

The Mode of Democracy with Chinese Characteristics——Based on the Mistake-tolerant Democracy

在新的历史发展时期,民主被提升到党的生命、社会主义生命和中华人民共和国生命的历史高度。但由于我国民主理论处于发展阶段,对新出现的法制改革比如深圳市、重庆市出台的试错条例尚缺乏解释力。有鉴于此,本文基于西方学者和中国传统儒家的错误观,提出了容错性民主新概念,并在批判西方民主理论的基础上,为深圳市、重庆市试错条例的出台提供理论依据。     

综合评价方法分类及研究进展

将各学科领域的综合评价方法归纳、分类,讨论了各类方法的基本原理、优缺点及适用 领域,并论述了综合评价方法研究的新进展. 指出目前综合评价存在的突出问题:多方法评价 结论的非一致性问题;方法针对性不强;理论研究与实际应用的脱节问题. 针对三个问题,提出 “基于方法集的组合评价”的新思路,以探索建立一个支持一致性的I3DSS.     

技术传统与传统技术的类型

技术的分类和分类标准的研究是技术哲学研究的基础问题。技术可以分为现代技术和传统技术,在此基础上,把传统技术区分为三种类型,即从属于僧侣的技术、从属于民众的技术和从属于宫廷的技术。这三种技术传统在历史上是相互独立、平行发展的。分类主要是依据“技术传统”的生存论特征,而且这种区分的标准不是根据技术的规范,而是依据不同技术传统整体的形而上学水平的差异和生存论特性的不同。这种分类对于技术的文化构成论的研究,对于完整地再现技术传统和我们人类的生存图景是恰当的。     

County child poverty rates in the US: a spatial regression approach

We apply methods of exploratory spatial data analysis (ESDA) and spatial regression analysis to examine intercounty variation in child poverty rates in the US. Such spatial analyses are important because regression models that exclude explicit specification of spatial effects, when they exist, can lead to inaccurate inferences about predictor variables. Using county-level data for 1990, we re-examine earlier published results [Friedman and Lichter (Popul Res Policy Rev 17:91–109, 1998)]. We find that formal tests for spatial autocorrelation among county child poverty rates confirm and quantify what is obvious from simple maps of such rates: the risk of a child living in poverty is not (spatially) a randomly distributed risk at the county level. Explicit acknowledgment of spatial effects in an explanatory regression model improves considerably the earlier published regression results, which did not take account of spatial autocorrelation. These improvements include: (1) the shifting of “wrong sign” parameters in the direction originally hypothesized by the authors, (2) a reduction of residual squared error, and (3) the elimination of any substantive residual spatial autocorrelation. While not without its own problems and some remaining ambiguities, this reanalysis is a convincing demonstration of the need for demographers and other social scientists to examine spatial autocorrelation in their data and to explicitly correct for spatial externalities, if indicated, when performing multiple regression analyses on variables that are spatially referenced. Substantively, the analysis improves the estimates of the joint effects of place-influences and family-influences on child poverty.

Small-area estimation in the presence of area-level correlated responses

The Fay-Herriot area-level model for correlated response data is augmented with a between-groups-of-domains effect. Correlated-response parameters of small-area estimates no longer need the assumption of spatial contiguity. A simulation shows that area-level correlated-response observations increase the efficiency of the estimates, but do not reduce the biases.     

Multivariate-multiple circular regression

We introduce a fully model-based approach of studying functional relationships between a multivariate circular-dependent variable and several circular covariates, enabling inference regarding all model parameters and related prediction. Two multiple circular regression models are presented for this approach. First, for an univariate circular-dependent variable, we propose the least circular mean-square error (LCMSE) estimation method, and asymptotic properties of the LCMSE estimators and inferential methods are developed and illustrated. Second, using a simulation study, we provide some practical suggestions for model selection between the two models. An illustrative example is given using a real data set from protein structure prediction problem. Finally, a straightforward extension to the case with a multivariate-dependent circular variable is provided.     

The characterization of Monte Carlo errors for the quantification of the value of forensic evidence

Recent developments in forensic science have lead to a proliferation of methods for quantifying the probative value of evidence by constructing a Bayes Factor that allows a decision-maker to select between the prosecution and defense models. Unfortunately, the analytical form of a Bayes Factor is often computationally intractable. A typical approach in statistics uses Monte Carlo integration to numerically approximate the marginal likelihoods composing the Bayes Factor. This article focuses on developing a generally applicable method for characterizing the numerical error associated with Monte Carlo integration techniques used in constructing the Bayes Factor. The derivation of an asymptotic Monte Carlo standard error (MCSE) for the Bayes Factor will be presented and its applicability to quantifying the value of evidence will be explored using a simulation-based example involving a benchmark data set. The simulation will also explore the effect of prior choice on the Bayes Factor approximations and corresponding MCSEs.     

Continuous single attribute control chart for Markov-dependent processes

Most of the times, the observations related to the quality characteristic of a process do not need to be independent. In such cases, control charts based on the assumption of independence of the observations are not appropriate. When the characteristic under study is qualitative, Markov model serves as a simple model to account for the dependency of the observations. For this purpose, we develop an attribute control chart under 100% inspection for a Markov dependent process by controlling the error probabilities. This chart consists of two sub-charts. For a given sample, depending upon the state of the last observation of previous sample (if any), one of these two will be used. Optimal values of the design parameters of the control chart are obtained. Chart’s performance is studied by using its capability (probability) of detecting a shift in process parameters.