03748nam a22005175i 4500 978-3-031-60769-1 DE-He213 20251030112311.0 cr nn 008mamaa 240613s2024 sz | s |||| 0|eng d 9783031607691 978-3-031-60769-1 10.1007/978-3-031-60769-1 doi QA276-280 PBT bicssc MAT029000 bisacsh PBT thema Naess, Arvid. author. aut http://id.loc.gov/vocabulary/relators/aut 78666 Applied Extreme Value Statistics [electronic resource] : With a Special Focus on the ACER Method / by Arvid Naess. 1st ed. 2024. Cham : Springer Nature Switzerland : Imprint: Springer, 2024. XIV, 268 p. 139 illus., 47 illus. in color. online resource. text txt rdacontent computer c rdamedia online resource cr rdacarrier text file PDF rda - Challenges of Applied Extreme Value Statistics -- Classical Extreme Value Theory -- The Peaks-Over-Threshold Method -- A Point Process Approach to Extreme Value Statistics -- The ACER Method -- Some Practical Aspects of Extreme Value Analyses -- Estimation of Extreme Values for Financial Risk Assessment -- The Upcrossing Rate via the Characteristic Function -- Monte Carlo Methods and Extreme Value Estimation -- Bivariate Extreme Value Distributions -- Space-Time Extremes of Random Fields -- A Case Study - Extreme Water Levels. This book does not focus solely on asymptotic extreme value distributions. In addition to the traditional asymptotic methods, it introduces a data-driven, computer-based method, which provides insights into the exact extreme value distribution inherent in the data, and which avoids asymptotics. It therefore differs from currently available texts on extreme value statistics in one very important aspect. The method described provides a unique tool for diagnostics, and for efficient and accurate extreme value prediction based on measured or simulated data. It also has straightforward extensions to multivariate extreme value distributions. The first half provides an introduction to extreme value statistics with an emphasis on applications. It includes chapters on classical asymptotic theories and threshold exceedance models, with many illustrative examples. The mathematical level is elementary and, to increase readability, detailed mathematical proofs have been avoided in favour of heuristic arguments. The second half presents in some detail specialized topics that illustrate the power and the limitations of the concepts discussed. With diverse applications to science, engineering and finance, the techniques described in this book will be useful to readers from many different backgrounds. Statistics. 65982 Statistics . 65982 Stochastic processes. 76770 Statistical Theory and Methods. Statistics in Engineering, Physics, Computer Science, Chemistry and Earth Sciences. 66911 Stochastic Processes. 76770 SpringerLink (Online service) Springer Nature eBook Printed edition: 9783031607684 Printed edition: 9783031607707 Printed edition: 9783031607714 https://doi.org/10.1007/978-3-031-60769-1 ZDB-2-SMA ZDB-2-SXMS 60713 60713