Extreme Value Modeling and Risk Analysis ─ Methods and Applications

Extreme events are critical in risk analysis in the fields such as finance, insurance, climate change and hydrology. Examples are maxima of temperature, wind speed, insurance claims, financial loss, and flood level. Extreme value theory plays a central role in risk management, extreme value modeling, and strategic adaptation. This book covers the latest applications of extreme value modeling and risk analysis. The book uses advanced statistical methodologies to demonstrate difference between extreme observations and the mean state data.

Jun Yan is a professor in the Department of Statistics at the University of Connecticut. He was previously an assistant professor at the University of Iowa. He received a Ph.D. in statistics from the University of Wisconsin–Madison. His research interests include spatial extremes, copulas, survival analysis, estimating equations, clustered data analysis, statistical computing, and applications in public health and environment.

Dipak K. Dey is a Board of Trustees Distinguished Professor in the Department of Statistics and associate dean of the College of Liberal Arts and Sciences at the University of Connecticut. He is an elected fellow of the International Society for Bayesian Analysis and American Association for the Advancement of Science, an elected member of the Connecticut Academy of Arts and Sciences and International Statistical Institute, and a fellow of the American Statistical Association and Institute of Mathematical Statistics. Dr. Dey is a co-editor and co-author of several books, including the Chapman & Hall/CRCBayesian Modeling in Bioinformatics and A First Course in Linear Model Theory. His research interests include Bayesian analysis, bioinformatics, biostatistics, computational statistics, decision theory, environmental statistics, multivariate analysis, optics, reliability and survival analysis, statistical shape analysis, and statistical genetics.