长江流域城市人口分布及空间相关性研究

本文选取长江流域104个城市人口样本作为研究对象,采用人口分布的结构指数和空间自相关分析方法,以及借用GIS技术来研究长江流域城市人口分布的空间动态特征和城市人口之间的功能联系。结果表明,长江流域城市人口空间结构呈均衡发展态势;人口密度分布呈现趋同趋势;城市人口与邻近城市人口之间的空间正相关要多于空间负相关;长江流域城市人口之间的空间关联性被分为八种类型。     

地区间个人收入差距的状况及特点(英文)

在地区收入差距的文献中,一直缺乏从个人收入角度出发的研究;而关于个人收入分配的研究,也很少考察地区差异。文章使用2005年全国1％人口普查数据,从个人收入角度描述了全国及（几种不同定义下的）地区间的个人收入差距,并分析了影响个人收入差距的因素,得到了一些有意义的结果。     

高斯与观测误差分布的发现

在数理统计的发展史上,最重要的事件也许就是观测误差分布的发现。统计学家们在寻找观测误差分布的过程中,创造了许多有用的统计理论和方法,不过观测误差分布发现的优先权最终属于伟大的德国科学家高斯。文章主要介绍了高斯发现观测误差分布的思考过程,并期望读者能从高斯天才的思想中获得有益的启迪。     

Modelling the effects of repellent chemicals on foraging bees

Summary.  Many pesticide sprays that are used for crop protection are harmful to honey-bees. It can therefore be beneficial to add to the spray chemical compounds that are repellent to bees, to discourage them from feeding on recently sprayed crops. Experiments were conducted using an artificial feeding station to assess the repellent effects of various compounds. In this system, bees arrive at the feeding station, choose between feeding dishes to which different chemicals have been added, feed for a variable period and then depart. The number of bees at each feeding dish is recorded at intervals of 1 min. We discuss the analysis of data from this type of experiment, based on a queuing theory model.     

A hierarchical model for extreme wind speeds

Summary.  A typical extreme value analysis is often carried out on the basis of simplistic inferential procedures, though the data being analysed may be structurally complex. Here we develop a hierarchical model for hourly gust maximum wind speed data, which attempts to identify site and seasonal effects for the marginal densities of hourly maxima, as well as for the serial dependence at each location. A Gaussian model for the random effects exploits the meteorological structure in the data, enabling increased precision for inferences at individual sites and in individual seasons. The Bayesian framework that is adopted is also exploited to obtain predictive return level estimates at each site, which incorporate uncertainty due to model estimation, as well as the randomness that is inherent in the processes that are involved.     

Maximum Likelihood Estimation in Gaussian Chain Graph Models under the Alternative Markov Property

Abstract.  The Andersson–Madigan–Perlman (AMP) Markov property is a recently proposed alternative Markov property (AMP) for chain graphs. In the case of continuous variables with a joint multivariate Gaussian distribution, it is the AMP rather than the earlier introduced Lauritzen–Wermuth–Frydenberg Markov property that is coherent with data-generation by natural block-recursive regressions. In this paper, we show that maximum likelihood estimates in Gaussian AMP chain graph models can be obtained by combining generalized least squares and iterative proportional fitting to an iterative algorithm. In an appendix, we give useful convergence results for iterative partial maximization algorithms that apply in particular to the described algorithm.     

RECENTERED AND RESCALED INSTRUMENTAL VARIABLE ESTIMATION OF TOBIT AND PROBIT MODELS WITH ERRORS IN VARIABLES

Since Durbin (1954) and Sargan (1958), instrumental variable (IV) method has long been one of the most popular procedures among economists and other social scientists to handle linear models with errors-in-variables. A direct application of this method to nonlinear errors-in-variables models, however, fails to yield consistent estimators.

This article restricts attention to Tobit and Probit models and shows that simple recentering and rescaling of the observed dependent variable may restore consistency of the standard IV estimator if the true dependent variable and the IV's are jointly normally distributed. Although the required condition seems rarely to be satisfied by real data, our Monte Carlo experiment suggests that the proposed estimator may be quite robust to the possible deviation from normality.     

Statistical Analysis of Marginal Count Failure Data

Manufacturers want to assess the quality andreliability of their products. Specifically, they want to knowthe exact number of failures from the sales transacted duringa particular month. Information available today is sometimesincomplete as many companies analyze their failure data simplycomparing sales for a total month from a particular departmentwith the total number of claims registered for that given month.This information—called marginal count data—is, thus,incomplete as it does not give the exact number of failures ofthe specific products that were sold in a particular month. Inthis paper we discuss nonparametric estimation of the mean numbersof failures for repairable products and the failure probabilitiesfor nonrepairable products. We present a nonhomogeneous Poissonprocess model for repairable products and a multinomial modeland its Poisson approximation for nonrepairable products. A numericalexample is given and a simulation is carried out to evaluatethe proposed methods of estimating failure probabilities undera number of possible situations.     

Non-parametric Estimation of the Residual Distribution

Consider a heteroscedastic regression model Y = m ( X ) +σ( X )ε, where the functions m and σ are smooth, and ε is independent of X . An estimator of the distribution of ε based on non-parametric regression residuals is proposed and its weak convergence is obtained. Applications to prediction intervals and goodness-of-fit tests are discussed.