Jan Beirlant

Reinsurance: Actuarial and Statistical Aspects provides a survey of both the academic literature in the field as well as challenges appearing in reinsurance practice and puts the two in perspective. The book is written for researchers with an interest in reinsurance problems, for graduate students with a basic knowledge of probability and statistics as well as for reinsurance practitioners. The focus of the book is on modelling together with the statistical challenges that go along with it. The discussed statistical approaches are illustrated alongside six case studies of insurance loss data sets, ranging from MTPL over fire to storm and flood loss data. Some of the presented material also contains new results that have not yet been published in the research literature. An extensive bibliography provides readers with links for further study.

Research in the statistical analysis of extreme values has flourished over the past decades: new probability models, inference and data analysis techniques have been introduced, while new application areas have been explored. Statistics of Extremes: Theory and Applications covers a wide range of models and applications, in particular in financial and actuarial risk management, a major area of interest and relevance to extreme value theory. Case studies are introduced in the first part of the book and are used throughout to show the application of each model discussed. The second part of the book covers advanced topics, such as multivariate and Bayesian modelling of extremes. *Provides comprehensive coverage of a growing area of research. *Provides a good balance of theory and real-world applications. *Includes coverage of time series, regression, multivariate and Bayesian modelling. *Introduces a number of case studies early in the book and develops the theory around them. *Illustrated with many data examples and plots. Statistics of Extremes: Theory and Applications will appeal to researchers and graduate students of applied probability and statistics, particularly those studying extreme values. It is also suitable for applied statisticians working in finance and insurance, pollution and climatology, geology, metallurgy, and engineering.