Titlebook: Applied Extreme Value Statistics; With a Special Focus Arvid Naess Book 2024 The Editor(s) (if applicable) and The Author(s), under exclusi

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Monte Carlo Methods and Extreme Value Estimation,asons for this increase. First, the computational power available today, even for a laptop computer, is formidable and steadily increasing. Second, the versatility of Monte Carlo methods make them very attractive as a way of obtaining solutions to stochastic problems. The drawback of Monte Carlo met

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Bivariate Extreme Value Distributions,iency, because the general multivariate case would easily embroil us in the necessity to roll out a heavy machinery of notation without contributing to a deeper understanding of the issues involved. For a discussion of the general multivariate case, the reader may consult the book by Beirlant et al.

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Book 2024ntary 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 a

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Some Practical Aspects of Extreme Value Analyses,ilable at the internet address: .). This package also allows for optimized fitting of parametric functions for prediction of long return period extreme values for the case of asymptotic Gumbel distributions, cf. Sect. 5.5, which totally dominates engineering applications.

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Bivariate Extreme Value Distributions,sily be extended to several dimensions, in particular, to two (Naess, A note on the bivariate ACER method. Preprint Statistics No. 01/2011, Department of Mathematical Sciences, Norwegian University of Science and Technology, Trondheim; 2011). By this fact, a vehicle is obtained for providing a nonpa

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