Böcker i Wiley Series in Probability and Statistics-serien

PRICING INSURANCE RISKA comprehensive framework for measuring, valuing, and managing riskPricing Insurance Risk: Theory and Practice delivers an accessible and authoritative account of how to determine the premium for a portfolio of non-hedgeable insurance risks and how to allocate it fairly to each portfolio component.The authors synthesize hundreds of academic research papers, bringing to light little-appreciated answers to fundamental questions about the relationships between insurance risk, capital, and premium. They lean on their industry experience throughout to connect the theory to real-world practice, such as assessing the performance of business units, evaluating risk transfer options, and optimizing portfolio mix.Readers will discover:* Definitions, classifications, and specifications of risk* An in-depth treatment of classical risk measures and premium calculation principles* Properties of risk measures and their visualization* A logical framework for spectral and coherent risk measures* How risk measures for capital and pricing are distinct but interact* Why the cost of capital, not capital itself, should be allocated* The natural allocation method and how it unifies marginal and risk-adjusted probability approaches* Applications to reserve risk, reinsurance, asset risk, franchise value, and portfolio optimizationPerfect for actuaries working in the non-life or general insurance and reinsurance sectors, Pricing Insurance Risk: Theory and Practice is also an indispensable resource for banking and finance professionals, as well as risk management professionals seeking insight into measuring the value of their efforts to mitigate, transfer, or bear nonsystematic risk.

This 1971 classic on linear models features material that can be understood by any statistician who understands matrix algebra and basic statistical methods.

Comprehensive Reference Work on Multivariate Analysis and Its ApplicationsThe first edition of this book, by Mardia, Kent and Bibby, has been widely used globally for over 40 years. This second edition brings many topics up to date, with a special emphasis on recent developments.A wide range of material in multivariate analysis is covered, including the classical themes of multivariate normal theory, multivariate regression, inference, multidimensional scaling, factoranalysis, cluster analysis and principal component analysis. The book also now covers modern developments such as graphical models, robust estimation, statistical learning, and high-dimensional methods. The book expertly blends theory and application, providing numerous worked examples and exercises at the end of each chapter. The reader is assumed to have a basic knowledge of mathematical statistics at an undergraduate level together with an elementary understanding of linear algebra. There are appendices which provide a background in matrix algebra, a summary of univariate statistics, a collection of statistical tables and a discussion of computational aspects. The work includes coverage of:* Basic properties of random vectors, normal distribution theory, and estimation* Hypothesis testing, multivariate regression, and analysis of variance* Principal component analysis, factor analysis, and canonical correlation analysis* Cluster analysis and multidimensional scaling* New advances and techniques, including statistical learning, graphical models and regularization methods for high-dimensional dataAlthough primarily designed as a textbook for final year undergraduates and postgraduate students in mathematics and statistics, the book will also be of interest to research workers and applied scientists.