講述了學習獨立同分布隨機變量和向量的極值現象的數學背景和隨機過程技巧。重在強調極值的三個重要的話題，規則變化函數的解析理論，點過程和隨機測度的概率論，度量空間概率測度的若收斂的漸進分布逼近之間的聯系。目次：基礎；吸引域和規范常數；收斂的質量；記錄和極過程；多變量極值。

PrefacePreliminariesUniform ConvergenceInverses of Monotone FunctionsConvergence to Types Theorem and Limit Distributions of MaximaRegularly Varying Functions of a Real Variable BasicsDeeper Results；Karamata』S TheoremExtensions of Regular Variation：兀．Variation．F．VariationDomains ofAttraction and Norming ConstantsDomain ofAttraction ofA(x)=expDomain ofAttractionDomain ofAttractionVon Mises ConditionsEquivalence Classes and Computation of Normalizing ConstantsQuality ofConvergenceMoment ConvergenceDensity ConvergenceLarge Deviations．Uniform Rates of Convergence to Extreme Value LawsUniform Rates of ConvergenceUniform Rates of ConvergencePoint ProcessesFundamentalsLaplace FunctionalsPoisson ProcessesDefinition and ConstructionTransformations of Poisson ProcessesVague ConvergenceWeak Convergence of Point Processes and Random MeasuresRecords and Extrema ProcessesStructure of RecordsLimit Laws for RecordsExtremal ProcessesWeak Convergence to Extremal Processes Skorohod SpacesWeak Convergence of Maximal Processes to ExtremalProcesses via Weak Convergence of Induced Point ProcessesExtreme Value Theory for Moving AveragesIndependence of k-Record ProcessesMultivariate ExtremesMax．Infinite DivisibilityAn Example：The Bivariate NormalCharacterizing Max．id DistributionsLimit Distributions for Multivariate ExtremesCharacterizing Max．Stable DistributionsDomains of Attraction；Multivariate Regular VariationIndependence and DependenceAssociationRefeInde