Extreme Value Modeling and Risk Analysis: Methods and Applications presents a broad overview of statistical modeling of extreme events along with the most recent methodologies and various applications. The book brings together background material and advanced topics, eliminating the need to sort through the massive amount of literature on the subject. After reviewing univariate extreme value analysis and multivariate extremes, the book explains univariate extreme value mixture modeling, threshold selection in extreme value analysis, and threshold modeling of non-stationary extremes. It presents new results for block-maxima of vine copulas, develops time series of extremes with applications from climatology, describes max-autoregressive and moving maxima models for extremes, and discusses spatial extremes and max-stable processes. The book then covers simulation and conditional simulation of max-stable processes; inference methodologies, such as composite likelihood, Bayesian inference, and approximate Bayesian computation; and inferences about extreme quantiles and extreme dependence. It also explores novel applications of extreme value modeling, including financial investments, insurance and financial risk management, weather and climate disasters, clinical trials, and sports statistics. Risk analyses related to extreme events require the combined expertise of statisticians and domain experts in climatology, hydrology, finance, insurance, sports, and other fields. This book connects statistical/mathematical research with critical decision and risk assessment/management applications to stimulate more collaboration between these statisticians and specialists.

Extreme Value Modeling and Risk Analysis: Methods and Applications presents a broad overview of statistical modeling of extreme events along with the most recent methodologies and various applications. The book brings together background material and advanced topics, eliminating the need to sort through the massive amount of literature on the subject. After reviewing univariate extreme value analysis and multivariate extremes, the book explains univariate extreme value mixture modeling, threshold selection in extreme value analysis, and threshold modeling of non-stationary extremes. It presents new results for block-maxima of vine copulas, develops time series of extremes with applications from climatology, describes max-autoregressive and moving maxima models for extremes, and discusses spatial extremes and max-stable processes. The book then covers simulation and conditional simulation of max-stable processes; inference methodologies, such as composite likelihood, Bayesian inference, and approximate Bayesian computation; and inferences about extreme quantiles and extreme dependence. It also explores novel applications of extreme value modeling, including financial investments, insurance and financial risk management, weather and climate disasters, clinical trials, and sports statistics. Risk analyses related to extreme events require the combined expertise of statisticians and domain experts in climatology, hydrology, finance, insurance, sports, and other fields. This book connects statistical/mathematical research with critical decision and risk assessment/management applications to stimulate more collaboration between these statisticians and specialists.

The book presents a set of novel, efficient and systematic concurrent multiscale optimization methods by considering the distribution of the material in macro-scale and the unit-cell configuration design in micro-scale simultaneously. Different from the traditional optimization method that is performed in a single scale, the proposed methods could generate a great deal of improvements in structural performance through the multiscale structure-material concurrent optimum design.The proposed theory and methods are related to statics, dynamics, thermoelastics and the coupling of different physical fields. Therefore, it provides a comprehensive designing scheme when multiple factors are taken into account. For example, the designing scheme can have a great significance on enhancing the structural performances under coupled multi-physical fields, such as load bearing capacity, vibration resistance ability, and safety under thermal stress and so on.Several numerical examples are highlighted in this unique volume based on practical engineering applications. The examples collectively demonstrate drastically improved designs featuring excellent unit-cell configuration and highly regular macroscale material distribution in a variety of industrial applications.

This book introduces the main theoretical findings related to copulas and shows how statistical modeling of multivariate continuous distributions using copulas can be carried out in the R statistical environment with the package copula (among others). Copulas are multivariate distribution functions with standard uniform univariate margins. They are increasingly applied to modeling dependence among random variables in fields such as risk management, actuarial science, insurance, finance, engineering, hydrology, climatology, and meteorology, to name a few. In the spirit of the Use R! series, each chapter combines key theoretical definitions or results with illustrations in R. Aimed at statisticians, actuaries, risk managers, engineers and environmental scientists wanting to learn about the theory and practice of copula modeling using R without an overwhelming amount of mathematics, the book can also be used for teaching a course on copula modeling.

The purpose of this research was to study three different wetland plant species and to compare the chlorinated solvent removal efficiency among each species. Each plant has a different characteristic favorable for chlorinated solvent degradation. Eleocharis erythropoda (Spike Rush) are plants with thin tube like leaves and large root mass. Carex Comosa (longhaired Sedge) has broad leaves and Scirpus Atrovirens (Green Bulrush) are broad leafed wetland plants with a long flowering stem. Previous research had shown that wetlands were effective at degradating PCE.