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.     

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.     

非线性飘移布朗运动的极值分布

本文研究了从x出发的非线性漂移布朗运动的极大值、极小值和首达时问题.利用测度变换以及布朗运动的一些重要性质,如反射原理,增量的独立性等,获得了两种极值分布函数的精确表达式,得到了首达时的分布函数.结果表明,线性漂移布朗运动的极大值极小值以及首达时的分布问题的有关结果是本文结论的推论,最后给出一个例子.     

Equality of Types for the Distribution of the Maximum for Two Values of n Implies Extreme Value Type

In this note, we prove a characterization of extreme value distributions. We show that, under some conditions, if the distribution of the maximum of n i.i.d. variables is of the same type for two distinct values of n then the distribution is one of the three extreme value types. This is an analogue of the well known result that if the sum of two i.i.d. random variables with finite second moment is of the same type as the original distribution then the distribution is Gaussian (Kagan et al., 1973). Our result was motivated by study of the m out of n bootstrap.     

Nonparametric estimation of the dependence function for a multivariate extreme value distribution

Understanding and modeling dependence structures for multivariate extreme values are of interest in a number of application areas. One of the well-known approaches is to investigate the Pickands dependence function. In the bivariate setting, there exist several estimators for estimating the Pickands dependence function which assume known marginal distributions [J. Pickands, Multivariate extreme value distributions, Bull. Internat. Statist. Inst., 49 (1981) 859-878; P. Deheuvels, On the limiting behavior of the Pickands estimator for bivariate extreme-value distributions, Statist. Probab. Lett. 12 (1991) 429-439; P. Hall, N. Tajvidi, Distribution and dependence-function estimation for bivariate extreme-value distributions, Bernoulli 6 (2000) 835-844; P. Capéraà, A.-L. Fougères, C. Genest, A nonparametric estimation procedure for bivariate extreme value copulas, Biometrika 84 (1997) 567-577]. In this paper, we generalize the bivariate results to p-variate multivariate extreme value distributions with p?2. We demonstrate that the proposed estimators are consistent and asymptotically normal as well as have excellent small sample behavior.     

极值理论及其在巨灾再保险定价中的应用

巨灾损失中往往存在极端值,一般统计分布对其拟合效果欠佳,本文运用极值理论对极端值建模,基于分层定价的思想,在不同的起赔点下对再保险超额损失部分的定价进行了探讨,并以洪水损失数据为例进行了实证研究,拟合了POT模型,得到了洪水再保险纯保费。     

On the limit behavior of extreme order statistics

Anderson and Sethuraman described a class of distributions on positive integers for which the sample maximum and other extreme order statistics are asymptotically concentrated on k values. We prove a similar result for general distributions. Bibliography: 4 titles. __________ Translated from Zapiski Nauchnykh Seminarov POMI, Vol. 320, 2004, pp. 106–109.     

Orthant tail dependence of multivariate extreme value distributions

The orthant tail dependence describes the relative deviation of upper- (or lower-) orthant tail probabilities of a random vector from similar orthant tail probabilities of a subset of its components, and can be used in the study of dependence among extreme values. Using the conditional approach, this paper examines the extremal dependence properties of multivariate extreme value distributions and their scale mixtures, and derives the explicit expressions of orthant tail dependence parameters for these distributions. Properties of the tail dependence parameters, including their relations with other extremal dependence measures used in the literature, are discussed. Various examples involving multivariate exponential, multivariate logistic distributions and copulas of Archimedean type are presented to illustrate the results.     

Relative extreme values and extremum-times of jump process: Upward type

Relative extreme values are defined by the supremum and minimum of a general jump process before its first time quitting from some state set, and relative extremum-times are defined by the first times reaching relative extreme values. The main objective of this paper is to find out the exact distributions and moments of them as the maximum of the set is up or equal to the process initial state. As especial cases, these results are applied to a general birth-death process and generalized birth-death processes. Supported by Science College Foundation of Tsinghua University     

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.     

Loss of information of a statistic for a family of non-regular distributions, II: more general case

In the paper of Akahira (Ann Inst Statist Math 48:349–364, 1996), it was shown that the second order asymptotic loss of information in reducing to a statistic consisting of extreme values and an asymptotically ancillary statistic vanished for a family of non-regular distributions whose densities have the same values and the sum of differential coefficients at the endpoints of the bounded support is equal to zero. In this paper, the result can be shown to be extended to the case of a family of non-regular distributions without the above restriction.     

Strong Domain of Attraction of Extreme Generalized Order Statistics

It is a well-known result in extreme value theory that the von Mises conditions imply the strong convergence of extreme order statistics. We extend this result to extreme generalized order statistics. A characterization of strong domains of attraction of joint distributions of a fixed number of extreme generalized order statistics by means of the corresponding result for generalized maxima is given. In particular, we determine the asymptotic joint distribution of (upper and lower) extreme generalized order statistics. Finally, we show that the Hill estimator based on extreme generalized order statistics is asymptotic normal.     

Repeat sampling of extreme observations: regression to the mean revisited

The phenomenon of classical regression to the mean was described by Sir Francis Galton in a series of prestigious works in the 19th century. This phenomenon refers to the fact that, in the presence of measurement error, experimental units which give rise to extreme values upon first sampling typically produce less extreme values upon a second independent (or repeat) sampling. This shift from the tails toward the population mean occurs even though there has been no intervention or change in the underlying population or error distributions. The mathematical ideas used to explain this shift typically appeal to correlation arguments and the classical Gaussian model. In this paper we study repeat sampling effects in the tails of arbitrary distributions. Perhaps surprisingly, we are able to show that there are actually three distinct asymptotic repeat sampling effects, of which only one corresponds to Galton’s classical result. These three effects depend on the heaviness of the population tails. In particular, for population distributions with relatively heavy tails the maximum shift occurs in the interior of the distribution. In this case the classical regression effect of Galton actually disappears out in the tails.     

Loss of information of a statistic for a family of non-regular distributions

In the non-regular case, the asymptotic loss of amount of information (extended to as Rényi measure) associated with a statistic is discussed. It is shown that the second order asymptotic loss of information in reducing to a statistic consisting of extreme values and an asymptotically ancillary statistic vanishes. This result corresponds to the fact that the statistic is second order asymptotically sufficient in the sense of Akahira (1991, Metron, 49, 133–143). Some examples on truncated distributions are also given.     

On the distributions of the ratios of the extreme roots to the trace of the Wishart matrix

In this paper, the authors cosider the derivation of the exact distributions of the ratios of the extreme roots to the trace of the Wishart matrix. Also, exact percentage points of these distributions are given and their applications are discussed.     

Extreme Values in ON/OFF Models of Teletraffic under Permanent and Periodic Measurements

The asymptotic behavior of stream intensity extreme values in ON/OFF models of teletraffic under permanent and periodic measurements is studied. It is assumed that the intensity of each source has a distribution with a heavy (regularly varying) tail. A joint limiting distribution for maxima with a common linear normalization, marginal distributions, and the distribution of the maxima ratio are obtained. The extremal index for a sequence of periodic measurements is calculated.     

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.     

关于拟合优度检验的EDF统计量的若干评注(英语)

拟合优度检验是建立统计模型的一个重要手段,很多检验统计量用一个理想样本能达到它们自己的极值,但EDF统计量做不到,这无疑会影响检验的势。在本文中,我们将提出某些调整型EDF统计量,它们具有这些性质,并改进了EDF检验,蒙得卡罗模拟表明,调整型EDF统计量在很多场合要必EDF具有更高的势,特别对重尾的备选分布更是这样,我们还考察了检验的形态与它们的极值点之间的关系。     

Models for Stationary Max-Stable Random Fields

Models for stationary max-stable random fields are revisited and illustrated by two-dimensional simulations. We introduce a new class of models, which are based on stationary Gaussian random fields, and whose realizations are not necessarily semi-continuous functions. The bivariate marginal distributions of these random fields can be calculated, and they form a new class of bivariate extreme value distributions.