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Extremes in Random Fields: A Theory and Its Applications

Benjamin Yakir
Format
Book
Published
Chichester, West Sussex, United Kingdom : John Wiley & Sons Inc., 2013.
Language
English
ISBN
9781118620205 (hardback), 1118620208 (hardback)
Related Resources
Cover image
Summary
"Reading chapters of the book can be used as a primer for a student who is then required to analyze a new problem that was not digested for him/her in the book"--
Description
xiii, 225 pages ; 24 cm
Notes
  • Machine generated contents note: Preface I Theory 1 Introduction 1.1 Distribution of extremes in random fields 1.2 Outline of the method 1.3 Gaussian and asymptotically Gaussian random fields 1.4 Applications 2 Basic Examples 2.1 Introduction 2.2 A power-one sequential test 2.3 A kernel-based scanning statistic 2.4 Other methods 3 Approximation of the Local Rate 3.1 Introduction 3.2 Preliminary localization and approximation 3.2.1 Localization 3.2.2 A discrete approximation 3.3 Measure transformation 3.4 Application of the localization theorem 3.5 Integration 4 From the Local to the Global 4.1 Introduction 4.2 Poisson approximation of probabilities 4.3 Average run length to false alarm 5 The Localization Theorem 5.1 Introduction 5.2 A simplifies version of the localization theorem 5.3 The Localization Theorem 5.4 A local limit theorem 5.5 Edge effects II Applications 6 Kolmogorov-Smirnov and Peacock 6.1 Introduction 6.2 Analysis of the one-dimensional case 6.3 Peacock's test 6.4 Relations to scanning statistics 7 Copy Number Variations 7.1 Introduction 7.2 The statistical model 7.3 Analysis of statistical properties 7.4 The False Discovery Rate (FDR) 8 Sequential Monitoring of an Image 8.1 Introduction 8.2 The statistical model 8.3 Analysis of statistical properties 8.4 Optimal change-point detection 9 Buffer Overflow 9.1 Introduction 9.2 The statistical model 9.3 Analysis of statistical properties 9.4 Long-range dependence and self-similarity 10 Computing Pickands' Constants 10.1 Introduction 10.2 Representations of constants 10.3 Analysis of statistical error 10.4 Local fluctuations Appendix A Mathematical Background A.1 Transforms A.2 Approximations of sum of independent random elements A.3 Concentration inequalities A.4 Random walks A.5 Renewal theory A.6 The Gaussian distribution A.7 Large sample inference A.8 Integration A.9 Poisson approximation A.10 Convexity References Index .
  • Includes bibliographical references and index.
Technical Details
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    a| Includes bibliographical references and index.
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    a| Machine generated contents note: Preface I Theory 1 Introduction 1.1 Distribution of extremes in random fields 1.2 Outline of the method 1.3 Gaussian and asymptotically Gaussian random fields 1.4 Applications 2 Basic Examples 2.1 Introduction 2.2 A power-one sequential test 2.3 A kernel-based scanning statistic 2.4 Other methods 3 Approximation of the Local Rate 3.1 Introduction 3.2 Preliminary localization and approximation 3.2.1 Localization 3.2.2 A discrete approximation 3.3 Measure transformation 3.4 Application of the localization theorem 3.5 Integration 4 From the Local to the Global 4.1 Introduction 4.2 Poisson approximation of probabilities 4.3 Average run length to false alarm 5 The Localization Theorem 5.1 Introduction 5.2 A simplifies version of the localization theorem 5.3 The Localization Theorem 5.4 A local limit theorem 5.5 Edge effects II Applications 6 Kolmogorov-Smirnov and Peacock 6.1 Introduction 6.2 Analysis of the one-dimensional case 6.3 Peacock's test 6.4 Relations to scanning statistics 7 Copy Number Variations 7.1 Introduction 7.2 The statistical model 7.3 Analysis of statistical properties 7.4 The False Discovery Rate (FDR) 8 Sequential Monitoring of an Image 8.1 Introduction 8.2 The statistical model 8.3 Analysis of statistical properties 8.4 Optimal change-point detection 9 Buffer Overflow 9.1 Introduction 9.2 The statistical model 9.3 Analysis of statistical properties 9.4 Long-range dependence and self-similarity 10 Computing Pickands' Constants 10.1 Introduction 10.2 Representations of constants 10.3 Analysis of statistical error 10.4 Local fluctuations Appendix A Mathematical Background A.1 Transforms A.2 Approximations of sum of independent random elements A.3 Concentration inequalities A.4 Random walks A.5 Renewal theory A.6 The Gaussian distribution A.7 Large sample inference A.8 Integration A.9 Poisson approximation A.10 Convexity References Index .
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    i| Online version: a| Yakir, Benjamin, author. t| Extremes in random fields d| Chichester, West Sussex, United Kingdom : John Wiley & Sons Inc., 2013 z| 9781118720615 w| (DLC) 2013025343
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