二元极值混合模型相关结构的研究

二元极值混合模型由于不能反映极值变量之间的完全相关性,因而在应用上受到了一定的限制,但对适当的相关性仍是一个很好的模型.本文给出了二元极值混合模型的一些基本性质,特别用随机模拟方法研究了对来自其它不同极值copula的随机样本,用混合模型拟合可能产生的影响. 结果表明,如果以Kendallτ表示变量间的相关性,在一定范围内,混合模型能够很好的反映其它模型所具有的相关性,且对渐近独立模型边际参数估计的偏差也不太大.最后应用混合条件分布与GEV条件分布分析英镑对美元和英镑对加元两支汇率日对数回报收益率的风险相关性.     

二元极值分布混合模型的矩估计

极值理论在各个领域得到了越来越多的关注和应用, 尤其是多元极值分布. 而矩估计是一种经典的参数估计方法, 计算简单且具有某些优良性, 本文给出边缘为标准指数分布的二元极值混合模型相关参数的矩估计及其渐近方差. 并将其与极大似然估计的渐近方差比较, 结果表明矩估计是一个较好的估计.     

A NOTE ON H(U)SLER-REISS DISTRIBUTION

本文研究了服从二元二项分布的随机向量序列的极值分布问题.利用构造性的方法,证明了服从二元二项分布的随机向量序列之极值依分布收敛到Hüsler-Reiss分布,将已有结论推广到离散情形.     

关于Hsler-Reiss分布的一点注记

本文研究了服从二元二项分布的随机向量序列的极值分布问题.利用构造性的方法,证明了服从二元二项分布的随机向量序列之极值依分布收敛到Hsler-Reiss 分布, 将已有结论推广到离散情形.     

例说基于可识最小值之识别性与参数估计及特征的关系

讨论基于可识最小值之识别性与参数估计及特征的关系,以二元Marshall-Olkin型Weibull分布为例,存在全部参数可估计且可识别且有识别特征的情形;以二元McKay型伽马分布为例,存在全部参数可估计且部分参数可识别且无识别特征而有其它分离特征的情形,若是基于可识最小值及差值,则是全部参数可估计且全部参数可识别且有识别特征的情形;以二元极值二点分布为例,存在部分参数可估计且部分参数可识别且有识别特征的情形.     

基于极值分布下混合联合位置与散度模型的参数估计

极值分布在金融工程、气象工程和其他领域中都有重要用途,本文提出基于极值分布下的混合联合位置与散度模型,通过EM算法给出该模型参数的极大似然估计.最后,通过随机模拟和实例研究说明该模型和方法是有用和有效的.     

两极端值模型的比较和应用

极端值模型主要有分块样本极大值模型和阈顶点模型.从两模型极值分布的内在关系、尾部特征的角度作比较分析和证明.结果表明,它们的内在关系一致;随着形状参数的变化尾部各有不同特征,阈顶点模型更为具体和多样,更适合金融风险度量的应用.     

基于POT方法的极值理论在基金净值预测中的应用

为对基金净值数据进行建模,根据基金净值样本数据的尾部特点,建立极大,极小值分布的GPD模型,运用POT方法确定临界值,进而对参数进行估计,并对模型进行检验.最后,运用建立的模型对一些极值点进行预测.所得结果很好地描述了数据特点,对极值点的预测符合实际.     

上证股指极值模型估计和VaR计算

POT极值模型参数的准确估计是计算金融资产回报厚尾分布市场风险的关键.由n阶概率加权矩得到参数的二项式回归估计,而将参数的零,一阶概率加权矩估计予以推广.极大似然估计中.将极大化似然函转化为二元函数无条件极值问题·其他参数估计方法的结果作为迭代的初始值,通过它们的似然函数值和极大似然函数值的比较以及迭代次数判断方法的优劣.实证研究表明:参数的零、一阶概率加权矩估计较接近于真值,随着阶数的提高,二项式回归参数估计的误差很大.参数的极大似然估计优于非线性回归估计优于零、一阶概率加权矩估计.在此基础上计算上证A股指数vaR值.     

带有二元连接函数的半参数模型

提出了一种新的带有二元连接函数的广义半参数模型,即二元连接模型(简称为BLM).使用轮廓似然方法估计模型的参数和非参数部分,并给出了计算算法.证明了所得的未知参数的估计量为n~(1/2)-相合,渐近正态且具有渐近最小方差,给出了实际数据分析和模拟研究,最终采用局部功效方法来检验非参数部分的线性性.     

Review of testing issues in extremes: in honor of Professor Laurens de Haan

As a leading statistician in extreme value theory, Professor Laurens de Haan has made significant contribution in both probability and statistics of extremes. In honor of his 70th birthday, we review testing issues in extremes, which include research done by Professor Laurens de Haan and many others. In comparison with statistical estimation in extremes, research on testing has received less attention. So we also point out some practical questions in this direction.      

Estimation of the Extreme Flow Distributions by Stochastic Models

The t-year event is a commonly used characteristic to describe the extreme flood peak in hydrological designs. The annual maximum series (AMS) and the partial duration series (PDS) are two basic approaches in flood analyses. In this paper, we first derive the distribution of the maximum extreme or the joint distribution of two or more maximum extremes from historical records based on a stochastic model, and then estimate statistical characteristics, including the t-year event, from the distribution. In addition to the two classical approaches (AMS and PDS), two additional approaches are proposed for estimating the unknown parameters in this paper. The first one uses two or more annual maximums (MAMS) as the sample to estimate the distribution of the maximum extremes. The second one uses multi-variate shock model to estimate the distribution of the maximum extremes for a multi-modal streamflow. The distribution of the extreme streamflow and the associated characteristics in the Bird Creek in Avant, Oklahoma, in the St. Johns River in Deland, Florida, and in the West Walker River in Coleville, California are estimated by using the stochastic model. To investigate further the performance of the estimation, the stochastic models based on AMS, MAMS and PDS related are also applied to the simulated data. The results show that the stochastic model and the related methods are reliable.     

A Spatial Markov Model for Climate Extremes

Spatial climate data are often presented as summaries of areal regions such as grid cells, either because they are the output of numerical climate models or to facilitate comparison with numerical climate model output. Extreme value analysis can benefit greatly from spatial methods that borrow information across regions. For Gaussian outcomes, a host of methods that respect the areal nature of the data are available, including conditional and simultaneous autoregressive models. However, to our knowledge, there is no such method in the spatial extreme value analysis literature. In this article, we propose a new method for areal extremes that accounts for spatial dependence using latent clustering of neighboring regions. We show that the proposed model has desirable asymptotic dependence properties and leads to relatively simple computation. Applying the proposed method to North American climate data reveals several local and continental-scale changes in the distribution of precipitation and temperature extremes over time. Supplementary material for this article is available online.     

Smoothing Sample Extremes with Dynamic Models

The study of extreme values is of crucial interest in many contexts. The concentration of pollutants, the sea-level and the closing prices of stock indexes are only a few examples in which the occurrence of extreme values may lead to important consequences. In the present paper we are interested in detecting trend in sample extremes. A common statistical approach used to identify trend in extremes is based on the generalized extreme value distribution, which constitutes a building block for parametric models. However, semiparametric procedures imply several advantages when exploring data and checking the model. This paper outlines a semiparametric approach for smoothing sample extremes, based on nonlinear dynamic modelling of the generalized extreme value distribution. The relative merits of this approach are illustrated through two real examples.AMS 2000 Subject Classification. Primary—62G32, 62G05, 62M10     

The exact distribution of extremes of a non-gaussian process

The exact distribution of extremes of a non-gaussian stationary discrete process is obtained and their crossing intervals are studied in terms of the autocorrelation coefficients for any level of crossing. This process is an important model for some physical magnitudes.     

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.     

Stereology of Extremes; Bivariate Models and Computation

The extremal shape factor of spheroidal particles is studied in the stereological context using the extreme value theory. The domain of attraction is invariant with respect to the transformation between spatial characteristics and planar sections characteristics. It is shown that for the Farlie-Gumbel-Morgenstern bivariate distribution of size and shape factor one can estimate the normalizing constants of shape factor conditioned by unknown particle size. The theoretical solution is followed by a detailed simulation study which demonstrates the use of estimation techniques developed. The method is useful for engineering applications in materials science, where microstructural extremes correlate with the properties of materials.     

Robust Estimation of the Generalized Pareto Distribution

One approach used for analyzing extremes is to fit the excesses over a high threshold by a generalized Pareto distribution. For the estimation of the shape and scale parameters in the generalized Pareto distribution, under some restrictions on the value of the scale parameter, maximum likelihood, method of moments and probability weighted moments' estimators are available. However, these are not robust estimators. In this paper we implement a robust estimation procedure known as the method of medians (He and Fung, 1999) to estimate the parameters in the generalized Pareto distribution. The asymptotic distribution of our estimator is normal for any value of the shape parameter except –1.     

Practical Extreme Value Modelling of Hydrological Floods and Droughts: A Case Study

Estimation of flood and drought frequencies is important for reservoir design and management, river pollution, ecology and drinking water supply. Through an example based on daily streamflow observations, we introduce a stepwise procedure for estimating quantiles of the hydrological extremes floods and droughts. We fit the generalised extreme value (GEV) distribution by the method of block maxima and the generalised Pareto (GP) distribution by applying the peak over threshold method. Maximum likelihood, penalized maximum likelihood and probability weighted moments are used for parameter estimation. We incorporate trends and seasonal variation in the models instead of splitting the data, and investigate how the observed number of extreme events, the chosen statistical model, and the parameter estimation method effect parameter estimates and quantiles. We find that a seasonal variation should be included in the GEV distribution fitting for floods using block sizes less than one year. When modelling droughts, block sizes of one year or less are not recommended as significant model bias becomes visible. We conclude that the different characteristics of floods and droughts influence the choices made in the extreme value modelling within a common inferential strategy.This revised version was published online in March 2005 with corrections to the cover date.