区域经济增长与环境质量演进关系模型研究——基于环境库兹涅茨曲线

从社会福利最大化角度出发,通过设定物品消费偏好指数,建立两物品模型,推导出了区域环境最佳投资水平;在此基础上,从理论上证明了环境库兹涅茨曲线存在的依据以及在环境质量最低点和环境质量发生根本性好转的转折点的社会总投资水平。最后,得出环境质量演化过程曲线取决于社会经济发展状况,它与非环境物品生产函数和排污生产函数的技术参数、居民收入水平以及对环境物品和非环境物品的消费偏好以及国民收入分配政策等有关,而非仅仅与经济增长状况有关。     

Semiparametric linear transformation models for current status data

Current status data arise when the death of every subject in a study cannot be determined precisely, but is known only to have occurred before or after a random monitoring time. The authors discuss the analysis of such data under semiparametric linear transformation models for which they propose a general inference procedure based on estimating functions. They determine the properties of the estimates they propose for the regression parameters of the model and illustrate their technique using tumorigenicity data.     

Generalized Leverage and its Applications

The generalized leverage of an estimator is defined in regression models as a measure of the importance of individual observations. We derive a simple but powerful result, developing an explicit expression for leverage in a general M -estimation problem, of which the maximum likelihood problems are special cases. A variety of applications are considered, most notably to the exponential family non-linear models. The relationship between leverage and local influence is also discussed. Numerical examples are given to illustrate our results     

A Multivariate Model for Repeated Failure Time Measurements

A parametric multivariate failure time distribution is derived from a frailty-type model with a particular frailty distribution. It covers as special cases certain distributions which have been used for multivariate survival data in recent years. Some properties of the distribution are derived: its marginal and conditional distributions lie within the parametric family, and association between the component variates can be positive or, to a limited extent, negative. The simple closed form of the survivor function is useful for right-censored data, as occur commonly in survival analysis, and for calculating uniform residuals. Also featured is the distribution of ratios of paired failure times. The model is applied to data from the literature     

Spatial variation in risk of disease: a nonparametric binary regression approach

A common problem in environmental epidemiology is the estimation and mapping of spatial variation in disease risk. In this paper we analyse data from the Walsall District Health Authority, UK, concerning the spatial distributions of cancer cases compared with controls sampled from the population register. We formulate the risk estimation problem as a nonparametric binary regression problem and consider two different methods of estimation. The first uses a standard kernel method with a cross-validation criterion for choosing the associated bandwidth parameter. The second uses the framework of the generalized additive model (GAM) which has the advantage that it can allow for additional explanatory variables, but is computationally more demanding. For the Walsall data, we obtain similar results using either the kernel method with controls stratified by age and sex to match the age–sex distribution of the cases or the GAM method with random controls but incorporating age and sex as additional explanatory variables. For cancers of the lung or stomach, the analysis shows highly statistically significant spatial variation in risk. For the less common cancers of the pancreas, the spatial variation in risk is not statistically significant.     

我国绿色基础设施的经济价值预算与分析

绿色基础设施是自然的生态基础设施和半自然的市政绿色空间的结合,它遵循自然发展的规律,在改善生态环境和保护生物多样性的同时,也蕴藏着新的经济增长点.本研究尝试构建绿色基础设施经济价值评价指标,测算全国各省的绿色基础设施经济价值(2004-2015),探究其区域分布状况,并进一步运用灰色模型预测2016年、2020年和2025年我国绿色基础设施的经济价值,以期提升人们对绿色基础设施的认识,为在城市规划中建设和完善绿色基础设施提供参考,促进生态环境与经济的良性循环,推动城市可持续发展.     

信息分析与预测产业化的法律环境分析

信息分析与预测产业化从产品商品化开始,这是一个庞大的系统工程。其商品化引发了一系列产业连带效应,促进其在服务方式、服务内容、管理体制等方面迅速与市场接轨,并整体向产业化进军。依据我国国情与国际惯例,必须吸收其它部门的立法经验与成果,建立整套完善的有中国特色的信息法律体系。     

Practical Maximum Pseudolikelihood for Spatial Point Patterns(with Discussion)

This paper describes a technique for computing approximate maximum pseudolikelihood estimates of the parameters of a spatial point process. The method is an extension of Berman & Turner's (1992) device for maximizing the likelihoods of inhomogeneous spatial Poisson processes. For a very wide class of spatial point process models the likelihood is intractable, while the pseudolikelihood is known explicitly, except for the computation of an integral over the sampling region. Approximation of this integral by a finite sum in a special way yields an approximate pseudolikelihood which is formally equivalent to the (weighted) likelihood of a loglinear model with Poisson responses. This can be maximized using standard statistical software for generalized linear or additive models, provided the conditional intensity of the process takes an 'exponential family' form. Using this approach a wide variety of spatial point process models of Gibbs type can be fitted rapidly, incorporating spatial trends, interaction between points, dependence on spatial covariates, and mark information.     

论《左传》“天德合一”的天命观——《左传》预言的本质

本文认为,《左传》“多叙鬼神之事,预言祸福之期”现象的产生不是偶然的,而是尚未脱离神职的史官,或者说与宗教文化有千丝万缕联系的史官文化在探究天(神)人关系时的必然现象。《左传》既大讲鬼神对人的控制,又渲染道德决定人的归宿,本文通过对这一看似矛盾现象的系统分析认为,《左传》通过对鬼神预言结果的道德阐释,以高超的技巧将看似杂乱无章的神秘预言纳入到了一个“天德合一”的模式中,从而建构了一个天命控制人类历史,道德决定人的命运的完整的天命观体系;这种把天命神学纳入历史道德领域的努力,是《左传》作者为解释春秋战国之际的历史变迁而做的理论准备,这种试图以建构体系的方式阐释天人关系、历史规律的尝试也是值得肯定的。     

Designing and integrating composite networks for monitoring multivariate gaussian pollution fields

Networks of ambient monitoring stations are used to monitor environmental pollution fields such as those for acid rain and air pollution. Such stations provide regular measurements of pollutant concentrations. The networks are established for a variety of purposes at various times so often several stations measuring different subsets of pollutant concentrations can be found in compact geographical regions. The problem of statistically combining these disparate information sources into a single 'network' then arises. Capitalizing on the efficiencies so achieved can then lead to the secondary problem of extending this network. The subject of this paper is a set of 31 air pollution monitoring stations in southern Ontario. Each of these regularly measures a particular subset of ionic sulphate, sulphite, nitrite and ozone. However, this subset varies from station to station. For example only two stations measure all four. Some measure just one. We describe a Bayesian framework for integrating the measurements of these stations to yield a spatial predictive distribution for unmonitored sites and unmeasured concentrations at existing stations. Furthermore we show how this network can be extended by using an entropy maximization criterion. The methods assume that the multivariate response field being measured has a joint Gaussian distribution conditional on its mean and covariance function. A conjugate prior is used for these parameters, some of its hyperparameters being fitted empirically.