This book, "An Introduction to Statistical Modeling of Extreme Values" by Stuart Coles, provides a comprehensive overview of extreme value theory and its applications. The author, from the Department of Mathematics at the University of Bristol, introduces the field, which focuses on modeling and understanding rare events rather than typical ones. The book covers the historical development of extreme value theory, from its early roots in the 20th century to its modern statistical applications. Key topics include classical block maxima models, threshold exceedance models, extensions to stationary and non-stationary sequences, point process modeling, and multivariate extreme value models. The book emphasizes a likelihood-based approach, which allows for more flexible and accurate modeling of extreme value data. It includes numerous examples from various fields such as oceanography, wind engineering, and finance to illustrate the practical applications of these models. The content is structured to be accessible to both statisticians and non-statisticians, with a focus on statistical detail and practical examples. The book also provides computational details, using the S-PLUS software for data analysis and includes datasets and functions available online. Additionally, it covers advanced topics like Bayesian inference and spatial extremes, making it a valuable resource for researchers and practitioners in extreme value modeling.This book, "An Introduction to Statistical Modeling of Extreme Values" by Stuart Coles, provides a comprehensive overview of extreme value theory and its applications. The author, from the Department of Mathematics at the University of Bristol, introduces the field, which focuses on modeling and understanding rare events rather than typical ones. The book covers the historical development of extreme value theory, from its early roots in the 20th century to its modern statistical applications. Key topics include classical block maxima models, threshold exceedance models, extensions to stationary and non-stationary sequences, point process modeling, and multivariate extreme value models. The book emphasizes a likelihood-based approach, which allows for more flexible and accurate modeling of extreme value data. It includes numerous examples from various fields such as oceanography, wind engineering, and finance to illustrate the practical applications of these models. The content is structured to be accessible to both statisticians and non-statisticians, with a focus on statistical detail and practical examples. The book also provides computational details, using the S-PLUS software for data analysis and includes datasets and functions available online. Additionally, it covers advanced topics like Bayesian inference and spatial extremes, making it a valuable resource for researchers and practitioners in extreme value modeling.