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Mathematics - Probability Theory and Stochastic Processes | Extreme Value Theory - An Introduction

Extreme Value Theory

An Introduction

de Haan, Laurens, Ferreira, Ana

2006, XVII, 417 p.

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Extreme Value Theory offers a careful, coherent exposition of the subject starting from the probabilistic and mathematical foundations and proceeding to the statistical theory. The book covers both the classical one-dimensional case as well as finite- and infinite-dimensional settings. All the main topics at the heart of the subject are introduced in a systematic fashion so that in the final chapter even the most recent developments in the theory can be understood. The treatment is geared toward applications.

The presentation concentrates on the probabilistic and statistical aspects of extreme values such as limiting results, domains of attraction and development of estimators without emphasizing related topics such as point processes, empirical distribution functions and Brownian motion. An appendix on regular variation has been added since some required results in that area are not available in book form. The usefulness of the statistical theory is shown by treating several case studies in detail.

The book is a thorough, accessible, self-contained, graduate level treatment of modern extreme value theory and some of its applications. It is aimed at graduate students and researchers and requires only maturity in mathematics and statistics.

Content Level » Professional/practitioner

Keywords » Brownian motion - Estimator - Excel - Stochastic processes - point process - statistics - stochastic process

Related subjects » Applications - Probability Theory and Stochastic Processes - Statistical Theory and Methods

Table of contents 

One-Dimensional Observations.- Limit Distributions and Domains of Attraction.- Extreme and Intermediate Order Statistics.- Estimation of the Extreme Value Index and Testing.- Extreme Quantile and Tail Estimation.- Advanced Topics.- Finite-Dimensional Observations.- Basic Theory.- Estimation of the Dependence Structure.- Estimation of the Probability of a Failure Set.- Observations That Are Stochastic Processes.- Basic Theory in C[0,1].- Estimation in C[0, 1].

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