Statistical Analysis of Extreme Values: From Insurance, Finance, Hydrology and Other FieldsThe statistical analysis of extreme data is important for various disciplines, including hydrology, insurance, finance, engineering and environmental sciences. This book provides a self-contained introduction to the parametric modeling, exploratory analysis and statistical interference for extreme values. The entire text of this third edition has been thoroughly updated and rearranged to meet the new requirements. Additional sections and chapters, elaborated on more than 100 pages, are particularly concerned with topics like dependencies, the conditional analysis and the multivariate modeling of extreme data. Parts I–III about the basic extreme value methodology remain unchanged to some larger extent, yet notable are, e.g., the new sections about 'An Overview of Reduced-Bias Estimation' (co-authored by M.I. Gomes), 'The Spectral Decomposition Methodology', and 'About Tail Independence' (co-authored by M. Frick), and the new chapter about 'Extreme Value Statistics of Dependent Random Variables' (co-authored by H. Drees). Other new topics, e.g., a chapter about 'Environmental Sciences', (co--authored by R.W. Katz), are collected within Parts IV–VI. |
Contents
select menus for handling and visualizing data plotting dis | 34 |
Data Visualize Distribution Visualize Distribution Estimate | 36 |
Diagnostic Tools | 37 |
Copyright | |
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analysis asymptotic bandwidth beta bivariate bootstrap censored data claim sizes cluster coefficient common df computed condition confidence intervals converse Weibull d-variate data set DEMO denote df F dialog box example exceedance df extremal index extreme value fitted flood Fréchet gamma Gaussian GP df GP model hazard function Hill estimator histogram iid random variables initial reserve kernel density likelihood location and scale maximum mean claim mean excess function menu MLE(EV MLE(GP mouse mode multivariate nonparametric normal df null hypothesis obtains option p-value parametric estimation parametric model Pareto df Pareto Distributions Pareto GP pertaining PML group Poisson distribution Poisson process premium procedure Q-Q plot quantile quantile function returns ruin probability sample df sample mean scale parameters scatterplot Section shape simulate standard statistical survivor function T-year threshold upper tail variance vector Visualize Weibull dfs Weibull distribution window XTREMES zero