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Actuarial science --- Actuarial science. --- Statistics --- Insurance --- Mathematics
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This book summarizes the state of the art in tree-based methods for insurance: regression trees, random forests and boosting methods. It also exhibits the tools which make it possible to assess the predictive performance of tree-based models. 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 numerical illustrations or case studies. All numerical illustrations are performed with the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. In particular, masters students in actuarial sciences and actuaries wishing to update their skills in machine learning will find the book useful. This is the second 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.
Actuarial science --- Insurance --- Linear models (Statistics) --- Regression analysis. --- Actuarial science.
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Life insurance --- Mathematics --- Mathematics. --- Insurance, Life --- Insurance --- Viatical settlements --- Actuarial science --- Actuarial science. --- Statistics
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Ce livre est consacré à la modélisation et à l'évaluation quantitative des risques en actuariat sur une période. Après un bref rappel des notions en théorie des probabilités, l'auteur présente les modèles de base en actuariat permettant de décrire le comportement des risques en assurance. La mutualisation et les méthodes d'agrégation de risques indépendants sont passées en revue tout comme les notions de base de simulation stochastique et les applications pour l'évaluation quantitative des risques. Une brève introduction aux ordres stochastiques univariés, utilisés pour comparer et expliquer q
Insurance --- Risk (Insurance) --- Mathematics. --- Business mathematics --- Actuarial science --- Risk
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Actuarial science --- Finance --- Business mathematics. --- Mathematical models. --- Mathematics.
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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.
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Many historians of insurance have commented on the disconnect between the rise of English life insurance companies in the early eighteenth century and the mathematics behind the sound pricing of life insurance products that was developed at about the same time. Insurance and annuity promoters typically ignored this mathematical work. Bellhouse explores this issue, and shows that the early mathematical work was not motivated by insurance but instead by the fair valuation of life contingent contracts related to property. Even the work of the mathematician James Dodson in the creation of the Equitable Life Assurance Society, offering sound actuarially based premiums, did not change the industry in any significant way. The tipping point was a crisis in 1770 in which the philosopher and mathematician Richard Price, as well as other mathematicians, showed that a dozen or more recently formed annuity societies could not meet their financial obligations and were inviable.
Life insurance --- Actuarial science --- Statistics --- Insurance --- Insurance, Life --- Viatical settlements --- History --- Mathematics
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"Actuarial Principles: Lifetables and Mortality Models explores the core of actuarial science: the study of mortality and other risks and applications. Including the CT4 and CT5 UK courses, but applicable to a global audience, this work lightly covers the mathematical and theoretical background of the subject to focus on real life practice. It offers a brief history of the field, why actuarial notation has become universal, and how theory can be applied to many situations. Uniquely covering both life contingency risks and survival models, the text provides numerous exercises (and their solutions), along with complete self-contained real-world assignments. Key Features: Provides detailed coverage of life contingency risks and survival models. Presents self-contained chapters with coverage of key topics from both practitioner and theoretical viewpoints. Includes numerous real world exercises that are accompanied by enlightening solutions. Covers useful background information on how and why the subject has evolved and developed."--Provided by publisher.
Insurance --- Mathematics. --- Business mathematics --- Actuarial science --- Actuaries. --- Mortality --- Life tables --- Mortality tables --- Mathematicians
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Actuaries --- Actuarial science --- Actuariat --- Actuaires --- Society of Actuaries --- Society of Actuaries.
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