有关顺序统计量的极大似然估计

讨论了一类参数空间受样本限制的极大似然估计问题.分析了随机变量分布的非零区域与似然函数定义域的对应关系,提出如果分布的非零区域受参数限制,则无论似然方程是否可解,参数的极大似然估计必然与样本顺序统计量X_((n))或X_((1))有关,并具体分析了似然估计一定等于、一定不等于和可能等于顺序统计量X_((n))(X_((1)))的三种情形,并给出了相应的判别条件.最后分析得出在第三种判别条件之下,似然估计是否取值于x_((n))(x_((1)))视具体的样本观测值决定.     

“Asymptotically Unbiased” Estimators of the Tail Index Based on External Estimation of the Second Order Parameter

In this paper we shall deal with the asymptotic and finite sample properties of asymptotically unbiased estimators of the tail index , based on external adequate estimators of the second order parameter . The behavior of the -estimator considered has indeed a high impact on the distributional properties of the final estimator of , and must be carefully chosen. As a by-product of the final study we present also the finite sample properties of a few -estimators available in the literature.     

基于伴随次序统计量的回归函数核估计的强相合性

本文基于二维随机样本 {(Xi,Yi) ,i≥ 1 }的伴随次序统计量Y[r,n] ,定义了回归函数的核估计 ,在一定条件下 ,获得了回归函数核估计的强相合性 ,推广了已有文献中的部分结果 .     

二阶广义分布参数系统的谱分布

应用泛函分析算子理论的方法研究了Hilbert空间中二阶广义分布参数系统的谱分布问题,利用有界线性算子的广义逆给出了所讨论问题的解及解的构造性表达式。这对研究二阶广义分布参数系统的镇定及渐进稳定性问题都有重要的理论价值。     

On Moments of Bivariate Order Statistics

In the present paper, we give the exact explicit expression for the product moments (of any order) of bivariate order statistics (o.s.) from any arbitrary continuous bivariate distribution function (d.f.). Furthermore, for any arbitrary bivariate uniform d.f., universal distribution-free bounds for the differences of any two different product moments (of order (1,1) or (-1,1)) are given.     

Estimation of a Support Curve via Order Statistics

This paper deals with nonparametric estimation of the boundary curve of the support of a bivariate density function. This estimation problem arises in various contexts, such as for example scatterpoint image analysis and frontier estimation in econometrics. The setup in this paper is a general one, allowing the bivariate density function to be infinite, bounded away from zero or zero at the boundary. Two estimators for the boundary curve are introduced, both based on order statistics. The asymptotic distribution of the estimators and their rate of convergence are established. Via a comparison of the rates of convergence we recommend which estimator to use in a particular situation. Both estimators can be used as an initial estimator in a two-stage procedure, designed for getting a better estimation. Simulation studies demonstrate the finite-sample behavior of the estimators and the proposed two-stage procedure. We illustrate the procedure on a data set on American electric utility companies.     

Estimation with Sequential Order Statistics from Exponential Distributions

The lifetime of an ordinary k-out-of-n system is described by the (n–k+1)-st order statistic from an iid sample. This set-up is based on the assumption that the failure of any component does not affect the remaining ones. Since this is possibly not fulfilled in technical systems, sequential order statistics have been proposed to model a change of the residual lifetime distribution after the breakdown of some component. We investigate such sequential k-out-of-n systems where the corresponding sequential order statistics, which describe the lifetimes of these systems, are based on one- and two-parameter exponential distributions. Given differently structured systems, we focus on three estimation concepts for the distribution parameters. MLEs, UMVUEs and BLUEs of the location and scale parameters are presented. Several properties of these estimators, such as distributions and consistency, are established. Moreover, we illustrate how two sequential k-out-of-n systems based on exponential distributions can be compared by means of the probability P(X < Y). Since other models of ordered random variables, such as ordinary order statistics, record values and progressive type II censored order statistics can be viewed as sequential order statistics, all the results can be applied to these situations as well.     

Statistics of extremes for IID data and breakthroughs in the estimation of the extreme value index: Laurens de Haan leading contributions

In the last decades there has been a shift from the parametric statistics of extremes for IID random variables, based on the probabilistic asymptotic results in extreme value theory, towards a semi-parametric approach, where the estimation of the right tail-weight, under a quite general framework, is of major importance. After a brief presentation of classical Gumbel’s block methodology and of later improvements in the parametric framework (multivariate and multi-dimensional extreme value models for largest observations and peaks over threshold approaches), we present a coordinated overview, over the last three decades, of the developments on the estimation of the extreme value index under a semiparametric framework. Laurens de Haan has been one of the leading scientists in the field, (co-)author of many seminal ideas, that he generously shared with dozens (literally) of colleagues and students, thus achieving one of the main goals in a scientist’s life: he gathered around him a bunch of colleagues united in the endeavour of building knowledge. The last section is a personal tribute to Laurens, who fully lives his ideal that co-operation is the heart of Science. To Laurens de Haan, a token of friendship.     

Hilbert空间中二阶广义分布参数系统的反馈控制与极点配置

应用泛函分析及算子理论方法讨论了Hilbert空间中二阶广义分布参数系统的反馈控制与极点配置问题,通过构造状态反馈的具体形式使所得闭环系统实现无限多个极点的配置;利用有界线性算子的广义逆给出了问题的解及解的构造性表达式;这对广义分布参数系统的极点配置研究具有重要的理论价值.     

Non-Parametric Estimation of the Limit Dependence Function of Multivariate Extremes

This paper presents a new estimation procedure for the limit distribution of the maximum of a multivariate random sample. This procedure relies on a new and simple relationship between the copula of the underlying multivariate distribution function and the dependence function of its maximum attractor. The obtained characterization is then used to define a class of kernel-based estimates for the dependence function of the maximum attractor. The consistency and the asymptotic distribution of these estimates are considered.     

关于非退化的二阶曲线的分类研究

从抛物线的一种判定方法出发,借助于欧氏平面上非退化的二阶曲线的度量性质,通过对欧氏平面上非退化二阶曲线类型的研究,探究出确定非退化二阶曲线类型的若干定理。     

On the Distribution of the Extremes of Unequally Correlated Normal Variables with Applications to Antedependent Cluster Data

Based on a small correlations approach, this paper deals with the probability distributions of the order statistics under a general Gaussian model with unequal (positive or negative) correlations. Illustrations are given for the cluster data those follow suitable antedependence models.     

二阶线性微分方程的可积性判据

文章研究二阶线性微分方程y″+p(x)y′+q(x)y=0可积性.通过寻找p(x),q(x)满足的关系式得到方程可积的充分条件.     

On the Convergence Rates of Extreme Generalized Order Statistics

A classical result of extreme value theory yields that in case of a linear normalization three possible types of limit distributions are possible. As proved recently a similar classification of the limit distributions holds for extreme generalized order statistics which provide a general concept of ordered random variables. In this paper, we derive results for the convergence rates of the nth and (n-r+1)st generalized order statistic, respectively. It turns out that the rate is highly influenced by the choice of the normalizing sequence. Moreover, we show that a uniform bound of order 1/n holds for underlying generalized Pareto distributions, whereas for the standard normal distribution the convergence might be very slow. Similar results for ordinary order statistics are included.     

二阶微分方程第二边值问题模糊状态的数值求解

本讨论二阶微分方程的第二边值问题具有模糊不确定性时，运用模糊仿真原理和差分方法，求其边值问题的数值解法。     

利用二阶方向导数证明极值的充分条件

介绍二元函数二阶方向导数的概念与计算方法.利用线性代数中的二次型知识,对二元函数在驻点处是否取得极值的充分性定理给出有几何意义的证明.     

Rates of Convergence for Bivariate Extremes

Under a second order regular variation condition, rates of convergence of the distribution of bivariate extreme order statistics to its limit distribution are given both in the total variation metric and in the uniform metric.     

基于伴随次序统计量的回归函数核估计的矩相合性

回归函数的核估计的大样本性质,多年来一直受到众多学者的关注,且早期的回归函数的核估计均是基于原样本{(Xi, Yi), i≥1},本文基于二维随机样本{(Xi, Yi), i≥1}的伴随次序统计量Y[r,n],定义了回归函数的核估计,在一定条件下,获得了回归函数核估计的r阶矩相合性,推广了已有文献中的部分结果.     

The Asymptotic Distribution Theory of Bivariate Order Statistics

In this paper the limit distribution function (d.f.) of general bivariate order statistics (o.s.) (extreme, intermediate and central) is studied by the notion of the exceedances of levels and characteristic function (c.f.) technique. The advantage of this approach is to give a simple and unified method to derive the limit d.f. of any bivariate o.s. The conditions under which the limit d.f. splits into the product of the limit marginals are obtained. Some illustrative examples are given.     

圆内二维均匀分布的参数估计

研究了圆内二维均匀分布的参数估计,利用次序统计量得到了圆的半径的估计量与置信区间.