Springer series in operations research and financial engineering,
ISSN of Series
1431-8598
INTERNAL BIBLIOGRAPHIES/INDEXES NOTE
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Includes bibliographical references and index.
CONTENTS NOTE
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Intro; Preface; Contents; 1 D-Norms; 1.1 Norms and D-Norms; 1.2 Examples of D-Norms; 1.3 Takahashi's Characterizations; 1.4 Convexity of the Set of D-Norms; 1.5 When Is an Arbitrary Norm a D-Norm?; 1.6 The Dual D-Norm Function; 1.7 Normed Generators Theorem; 1.8 Metrization of the Space of D-Norms; 1.9 Multiplication of D-Norms; 1.10 The Functional D-Norm; 1.11 D-Norms from a Functional Analysis Perspective; Introducing the Relevant Space of Seminorms; 1.12 D-Norms from a Stochastic Geometry Perspective; 2 D-Norms & Multivariate Extremes; 2.1 Univariate Extreme Value Theory
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2.2 Multivariate Generalized Pareto DistributionsGeneral Multivariate GPDS; 2.3 Multivariate Max-Stable Distributions; 2.4 How to Generate Max-Stable RVS; 2.5 Covariances, Range, etc. of Standard Max-Stable rvs; 2.6 Max-Stable Random Vectors as Generators of D-Norms; 3 Copulas & Multivariate Extremes; 3.1 Characterizing Multivariate Domain of Attraction; 3.2 Multivariate Piecing-Together; 3.3 Copulas Not in the Domain of Attraction; 4 An Introduction to Functional Extreme Value Theory; 4.1 Generalized Pareto Processes; Defining a Simple Generalized Pareto Process; 4.2 Max-Stable Processes
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4.3 Generalized Max-Linear Models5 Further Applications of D-Norms to Probability & Statistics; 5.1 Max-Characteristic Function; 5.2 Multivariate Order Statistics: The Intermediate Case; 5.3 Multivariate Records and Champions; Simple Records; References; Index
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SUMMARY OR ABSTRACT
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This monograph compiles the contemporary knowledge about D-norms and provides an introductory tour through the essentials of multivariate extreme value theory. Following a clear introduction of D-norms, this book introduces links with the theory through multivariate generalized Pareto distributions and max stable distributions. Further views on D-norms from a functional analysis perspective and from stochastic geometry underline the aim of this book to reveal mathematical structures. This book is intended for mathematicians with a basic knowledge of analysis and probability theory, including Fubini's theorem.