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| 內容簡介： |
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本书讲述了学习独立同分布随机变量和向量的极值现象的数学背景和随机过程技巧。重在强调极值的三个重要的话题，规则变化函数的解析理论，点过程和随机测度的概率论，度量空间概率测度的若收敛的渐进分布逼近之间的联系。目次：基础；吸引域和规范常数；收敛的质量；记录和极过程；多变量极值。
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| 目錄：
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Preface
Preliminaries
Uniform Convergence
Inverses of Monotone Functions
Convergence to Types Theorem and Limit Distributions of
Maxima
Regularly Varying Functions of a Real Variable Basics
Deeper Results；Karamata’S Theorem
Extensions of Regular Variation：兀．Variation．F．Variation
Domains ofAttraction and Norming Constants
Domain ofAttraction ofAx=exp
Domain ofAttraction
Domain ofAttraction
Von Mises Conditions
Equivalence Classes and Computation of Normalizing Constants
Quality ofConvergence
Moment Convergence
Density Convergence
Large Deviations．
Uniform Rates of Convergence to Extreme Value Laws
Uniform Rates of Convergence
Uniform Rates of Convergence
Point Processes
Fundamentals
Laplace Functionals
Poisson Processes
Definition and Construction
Transformations of Poisson Processes
Vague Convergence
Weak Convergence of Point Processes and Random Measures
Records and Extrema Processes
Structure of Records
Limit Laws for Records
Extremal Processes
Weak Convergence to Extremal Processes Skorohod Spaces
Weak Convergence of Maximal Processes to Extremal
Processes via Weak Convergence of Induced Point Processes
Extreme Value Theory for Moving Averages
Independence of k-Record Processes
Multivariate Extremes
Max．Infinite Divisibility
An Example：The Bivariate Normal
Characterizing Max．id Distributions
Limit Distributions for Multivariate Extremes
Characterizing Max．Stable Distributions
Domains of Attraction；Multivariate Regular Variation
Independence and Dependence
Association
Refe
Inde
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