Moment Estimation for Multivariate Extreme Value Distribution in a Nested Logistic Model

This paper considers multivariate extreme value distribution in a nested logistic model. The dependence structure for this model is discussed. We find a useful transformation that transformed variables possess the mixed independence. Thus, the explicit algebraic formulae for a characteristic function and moments may be given. We use the method of moments to derive estimators of the dependence parameters and investigate the properties of these estimators in large samples via asymptotic theory and in finite samples via computer simulation. We also compare moment estimation with a maximum likelihood estimation in finite sample sizes. The results indicate that moment estimation is good for all practical purposes.     

Simulating Multivariate Extreme Value Distributions of Logistic Type

Methods are given for simulating from symmetric and asymmetric versions of the multivariate logistic distribution, and from other multivariate extreme value distributions based on the well known logistic model. We consider two general approaches. The first approach uses transformations to derive random variables with a joint distribution function from which it is easy to simulate. The second approach derives from a specification of conditionally independent marginal components, conditioning on positive stable random variables. This specification extends to models of nested or hierarchical type and leads to an efficient way of incorporating marginal censoring. The algorithms presented in Sections 2 and 3 are available on request from the author. They are also included in the R (Ihaka and Gentleman, 1996) package evd (Stephenson, 2002), which is available from http://www.maths.lancs.ac.uk/~stephena/.     

Inequalities for the Extremal Coefficients of Multivariate Extreme Value Distributions

The extremal coefficients are the natural dependence measures for multivariate extreme value distributions. For an m-variate distribution 2^m distinct extremal coefficients of different orders exist; they are closely linked and therefore a complete set of 2^m coefficients cannot take any arbitrary values. We give a full characterization of all the sets of extremal coefficients. To this end, we introduce a simple class of extreme value distributions that allows for a 1-1 mapping to the complete sets of extremal coefficients. We construct bounds that higher order extremal coefficients need to satisfy to be consistent with lower order extremal coefficients. These bounds are useful as lower order extremal coefficients are the most easily inferred from data.     

多变量极值理论在银行操作风险度量中的运用

针对银行操作风险损失分布的厚尾性和损失事件之间的尾部相依性,首先用单变量极值理论建立了单个损失事件计量模型,然后用多变量极值的连接函数反映了损失事件之间的尾部相依性,避免了计量中对银行操作风险的低估和对监管资本要求高估.     

On Domains of Attraction of Multivariate Extreme Value Distributions under Absolute Continuity

The paper gives sufficient conditions for domains of attraction of multivariate extreme value distributions. Under the assumption of absolute continuity of a multivariate distribution, the criteria enable one to examine, by using limits of some rescaled conditional densities, whether the distribution belongs to the domain of attraction of some multivariate extreme value distribution. If this is the case, the criteria also determine how to construct such an extreme value distribution. Unlike the criterion given by de Haan and Resnick [1987,Stochastic Process. Appl.2583–93], the criteria are easily applicable even when the marginal tails are not Pareto-like.     

一类多元函数极值的快速判别方法及应用

本文给出一类多元函数—三元函数是否存在极值的快速判别方法 ,并讨论它在实际问题中的应用 .     

Extreme Values in FGM Random Sequences

We consider the multivariate Farlie–Gumbel–Morgenstern class of distributions and discuss their properties with respect to the extreme values. This class was used to consider dependence in multivariate distributions and their ordering. We show that the extreme values of these distributions behave as if no dependence would exist between its components.     

Linear and Algebraic Independence of q-Zeta Values

In the paper, results on linear and algebraic independence of q-series of the form $\varsigma _q (s) = \sum\nolimits_{n = 1}^\infty {\sigma _{s - 1} (n)q^n }$ over the field ?(q) are obtained, where $\sigma _{s - 1} (n) = \sum\nolimits_{d|n} {d^{s - 1} }$ , s = 1, 2,... .     

Dependence Measures for Extreme Value Analyses

Quantifying dependence is a central theme in probabilistic and statistical methods for multivariate extreme values. Two situations are possible: one where, in a limiting sense, the extremes are dependent; the other where, in the same sense, the extremes are independent. This paper comprises an overview of the principal issues through a unified approach which encompasses both these situations. Novel diagnostic measures for dependence are also developed which provide complementary information about different aspects of extremal dependence. The paper is written in an elementary style, with the methodology illustrated by application to theoretical examples and typical data-sets. These data-sets and the S-plus functions used for the analyses are available online.     

Effects of Mis-Specification in Bivariate Extreme Value Problems

The need to incorporate the structure of complex problems in extreme value analyzes, and the requirement to exploit all the limited information that is available, has led to the increased use of advanced dependence models. When they are appropriate, these dependence models can lead to substantial benefits over simpler univariate extreme value methods. Here we explore some inference problems for the marginal and conditional distributions caused by model mis-specification. We find distinct differences in estimation characteristics when the dependence structure is asymptotically dependent or asymptotically independent, and that conditional models can be substantially improved if the variables are standardized to have common marginal distributions.     

MULTIVARIATE EXTREME VALUE DISTRIBUTION AND ITS FISHER INFORMATION MATRIX

ＭＵＬＴＩＶＡＲＩＡＴＥＥＸＴＲＥＭＥＶＡＬＵＥＤＩＳＴＲＩＢＵＴＩＯＮＡＮＤＩＴＳＦＩＳＨＥＲＩＮＦＯＲＭＡＴＩＯＮＭＡＴＲＩＸ￥ＳＨＩＤＡＯＪＩ（史道济）（ＤｅｐａｒｔｍｅｎｔｏｆＭａｔｈｅｍａｔｉｃｓ，ＴｉａｎｊｉｎＵｎｉｖｅｒｓｉｔｙ，Ｔｉａ...     

多元极值分布参数的最大似然估计与分步估计

本文考虑多元极值分布的参数估计，给出了分步估计渐近协差阵的近似表示，并对维数P=2，5及相关参数α=0．001，0．01；0．1（0．2），0．9；0．99，0．999的各种组合，计算了分步估计关于最大似然估计的渐近效率，分析了各种参数及维数对渐近效率的影响．分步估计是一种合理、简单而且有较强实用意义的估计方法．     

Likelihood-Based Inference for Extreme Value Models

Estimation of the extremal behavior of a process is often based on the fitting of asymptotic extreme value models to relatively short series of data. Maximum likelihood has emerged as a flexible and powerful modeling tool in such applications, but its performance with small samples has been shown to be poor relative to an alternative fitting procedure based on probability weighted moments. We argue here that the small-sample superiority of the probability weighted moments estimator is due to the assumption of a restricted parameter space, corresponding to finite population moments. To incorporate similar information in a likelihood-based analysis, we propose a penalized maximum likelihood estimator that retains the modeling flexibility and large-sample optimality of the maximum likelihood estimator, but improves on its small-sample properties. The properties of the penalized likelihood estimator are verified in a simulation study, and in application to sea-level data, which also enables the procedure to be evaluated in the context of structural models for extremes.     

Extreme value modeling under power normalization

In this paper the mathematical modeling of extremes under power normalization is developed. An estimate of the shape parameter within the generalized extreme value distribution under power normalization is suggested. The statistical inference about the upper tail of a distribution function by using the power normalization is studied. Two models for generalized Pareto distribution under power normalization (GPDP) are given. Estimates for the shape and scale parameters within these GPDP’s are obtained. Finally, a simulation study illustrates and corroborates theoretical results.     

基于矩阵初等变换的n元函数极值的快速判别法

给出了 n元函数极值的一个充分条件 ,并结合矩阵的初等变换建立了 n元函数极值的一种快速判别法 ,最后给出了一个例子     

基于n次型正定性的多元函数极值判别法

针对多元函数稳定点处二阶偏导数全为0的情况,提出了有效的极值判别法.定义了广义n维方阵、n次型及其正定性;提出了更具普遍意义的极值充分条件;得到了利用n次型的正定性判断n元函数极值的方法并举例验证了结论的正确性和有效性.     

关于条件极值的两点思考

认为现行高等数学教材关于多元函数条件极值的处理存在值得商榷之处.实例分析多元函数条件极值的拉格朗日乘数法和代人法.指出它们都必须受条件函数梯度非零的限制.利用已知目标函数和条件函数的一阶、二阶偏导数可以判定拉格朗日乘数法所得出的可能极值点处是取极大值还是极小值.由此可得判定条件极值的一个充分条件.     

Approximations for Bivariate Extreme Values

We study the tail behavior of distributions in the domain of attraction of bivariate extreme value distributions (this includes bivariate extreme value distributions themselves). We provide results on finite approximations of the tail behavior and its analytical shape. The results could form a basis to improve current statistical modeling of bivariate extreme values.     

Multivariate Distributions from Mixtures of Max-Infinitely Divisible Distributions

A class of multivariate distributions that are mixtures of the positive powers of a max-infinitely divisible distribution are studied. A subclass has the property that all weighted minima or maxima belong to a given location or scale family. By choosing appropriate parametric families for the mixing distribution and the distribution being mixed, families of multivariate copulas with a flexible dependence structure and with closed form cumulative distribution functions are obtained. Some dependence properties of the class, as well as some characterizations, are given. Conditions for max-infinite divisibility of multivariate distributions are obtained.