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This book summarizes the state of the art in generalized linear models (GLMs) and their various extensions: GAMs, mixed models and credibility, and some nonlinear variants (GNMs). In order to deal with tail events, analytical tools from Extreme Value Theory are presented. Going beyond mean modeling, it considers volatility modeling (double GLMs) and the general modeling of location, scale and shape parameters (GAMLSS). Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and case studies, providing numerical illustrations using the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. This is the first of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.

Actuarial science --- Insurance --- Linear models (Statistics) --- Actuarial science. --- Statistics . --- Actuarial Sciences. --- Statistics for Business, Management, Economics, Finance, Insurance.

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Health Insurance aims at filling a gap in actuarial literature, attempting to solve the frequent misunderstanding in regards to both the purpose and the contents of health insurance products (and ‘protection products’, more generally) on the one hand, and the relevant actuarial structures on the other. In order to cover the basic principles regarding health insurance techniques, the first few chapters in this book are mainly devoted to the need for health insurance and a description of insurance products in this area (sickness insurance, accident insurance, critical illness covers, income protection, long-term care insurance, health-related benefits as riders to life insurance policies). An introduction to general actuarial and risk-management issues follows. Basic actuarial models are presented for sickness insurance and income protection (i.e. disability annuities). Several numerical examples help the reader understand the main features of pricing and reserving in the health insurance area. A short introduction to actuarial models for long-term care insurance products is also provided. Advanced undergraduate and graduate students in actuarial sciences; graduate students in economics, business and finance; and professionals and technicians operating in insurance and pension areas will find this book of benefit.

wiskunde --- Mathematics --- Health insurance --- Assurance-maladie --- Finance --- EPUB-LIV-FT LIVMATHE LIVSTATI SPRINGER-B --- Mathematics. --- Actuarial science. --- Actuarial Sciences. --- Statistics --- Insurance --- Math --- Science --- Finance.

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Reinsurance is an important production factor of non-life insurance. The efficiency and the capacity of the reinsurance market directly regulate those of insurance markets. The purpose of this book is to provide a concise introduction to risk theory, as well as to its main application procedures to reinsurance.  The first part of the book covers risk theory. It presents the most prevalent model of ruin theory, as well as a discussion on insurance premium calculation principles and the mathematical tools that enable portfolios to be ordered according to their risk levels.  The second part describes the institutional context of reinsurance. It first strives to clarify the legal nature of reinsurance transactions. It describes the structure of the reinsurance market and then the different legal and technical features of reinsurance contracts, known as reinsurance ‘treaties’ by practitioners.   The third part creates a link between the theories presented in the first part and the practice described in the second one. Indeed, it sets out, mostly through examples, some methods for pricing and optimizing reinsurance. The authors' aim is to apply the formalism presented in the first part to the institutional framework given in the second part. It is reassuring to find such a relationship between approaches, seemingly abstract, and solutions adopted by practitioners.  Risk Theory and Reinsurance is mainly aimed at graduate students in actuarial science but will also be useful for practitioners wishing to revive their knowledge of risk theory or to quickly learn about the main mechanisms of reinsurance.

Actuarial science. --- Rinsurance. --- Mathematics. --- Actuarial Sciences. --- Statistics --- Insurance --- Math --- Science --- Mathematics --- Risk (Insurance) --- Reinsurance --- Mathematical models. --- Mathématiques --- EPUB-LIV-FT LIVMATHE LIVSTATI SPRINGER-B --- Actuarial science --- Mathematical models --- Reinsurance.

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Time Series Analysis With Applications in R, Second Edition, presents an accessible approach to understanding time series models and their applications. Although the emphasis is on time domain ARIMA models and their analysis, the new edition devotes two chapters to the frequency domain and three to time series regression models, models for heteroscedasticity, and threshold models. All of the ideas and methods are illustrated with both real and simulated data sets. A unique feature of this edition is its integration with the R computing environment. The tables and graphical displays are accompanied by the R commands used to produce them. An extensive R package, TSA, which contains many new or revised R functions and all of the data used in the book, accompanies the written text. Script files of R commands for each chapter are available for download. There is also an extensive appendix in the book that leads the reader through the use of R commands and the new R package to carry out the analyses. Jonathan Cryer is Professor Emeritus, University of Iowa, in the Department of Statistics and Actuarial Science. He is a Fellow of the American Statistical Association and received a Collegiate Teaching Award from the University of Iowa College of Liberal Arts and Sciences. He is the author of Statistics for Business: Data Analysis and Modeling, Second Edition, (with Robert B. Miller), the Minitab Handbook, Fifth Edition, (with Barbara Ryan and Brian Joiner), the Electronic Companion to Statistics (with George Cobb), Electronic Companion to Business Statistics (with George Cobb) and numerous research papers. Kung-Sik Chan is Professor, University of Iowa, in the Department of Statistics and Actuarial Science. He is a Fellow of the American Statistical Association and the Institute of the Mathematical Statistics, and an Elected Member of the International Statistical Institute. He received a Faculty Scholar Award from the University of Iowa in 1996. He is the author of Chaos: A Statistical Perspective (with Howell Tong) and numerous research papers.

statistisch onderzoek --- Mathematical statistics --- Time-series analysis --- R (Computer program language) --- Data processing --- Probabilities. --- Statistics . --- Actuarial science. --- R (Computer program language). --- Probability Theory and Stochastic Processes. --- Statistical Theory and Methods. --- Actuarial Sciences. --- Data processing.

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Artificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance. The third volume of the trilogy simultaneously introduces the relevant tools for developing and analyzing neural networks, in a style that is mathematically rigorous and yet accessible. The authors proceed by successive generalizations, requiring of the reader only a basic knowledge of statistics. Various topics are covered from feed-forward networks to deep learning, such as Bayesian learning, boosting methods and Long Short Term Memory models. All methods are applied to claims, mortality or time-series forecasting. This book is written for masters students in the actuarial sciences and for actuaries wishing to update their skills in machine learning. .

Actuarial science --- Insurance --- Neural networks (Computer science) --- Actuarial science. --- Statistics . --- Neural networks (Computer science) . --- Actuarial Sciences. --- Statistics for Business, Management, Economics, Finance, Insurance. --- Mathematical Models of Cognitive Processes and Neural Networks.