Effective Statistical Learning Methods for Actuaries II
Effective Statistical Learning Methods for Actuaries II

Author: Michel Denuit

Publisher: Springer Nature

Published: 2020-11-16

Total Pages: 228

ISBN-13: 303057556X

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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, master's 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.

Effective Statistical Learning Methods for Actuaries III
Effective Statistical Learning Methods for Actuaries III

Author: Michel Denuit

Publisher: Springer Nature

Published: 2019-10-31

Total Pages: 250

ISBN-13: 3030258270

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This book reviews some of the most recent developments in neural networks, with a focus on applications in actuarial sciences and finance. It simultaneously introduces the relevant tools for developing and analyzing neural networks, in a style that is mathematically rigorous yet accessible. Artificial intelligence and neural networks offer a powerful alternative to statistical methods for analyzing data. 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. Requiring only a basic knowledge of statistics, this book is written for masters students in the actuarial sciences and for actuaries wishing to update their skills in machine learning. This is the third 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.

Statistical Foundations of Actuarial Learning and its Applications
Statistical Foundations of Actuarial Learning and its Applications

Author: Mario V. Wüthrich

Publisher: Springer Nature

Published: 2022-11-22

Total Pages: 611

ISBN-13: 303112409X

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This open access book discusses the statistical modeling of insurance problems, a process which comprises data collection, data analysis and statistical model building to forecast insured events that may happen in the future. It presents the mathematical foundations behind these fundamental statistical concepts and how they can be applied in daily actuarial practice. Statistical modeling has a wide range of applications, and, depending on the application, the theoretical aspects may be weighted differently: here the main focus is on prediction rather than explanation. Starting with a presentation of state-of-the-art actuarial models, such as generalized linear models, the book then dives into modern machine learning tools such as neural networks and text recognition to improve predictive modeling with complex features. Providing practitioners with detailed guidance on how to apply machine learning methods to real-world data sets, and how to interpret the results without losing sight of the mathematical assumptions on which these methods are based, the book can serve as a modern basis for an actuarial education syllabus.

Effective Statistical Learning Methods for Actuaries I
Effective Statistical Learning Methods for Actuaries I

Author: Michel Denuit

Publisher: Springer Nature

Published: 2019-09-03

Total Pages: 452

ISBN-13: 3030258203

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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.

Effective Statistical Learning Methods for Actuaries I
Effective Statistical Learning Methods for Actuaries I

Author: Michel Denuit

Publisher:

Published: 2019

Total Pages: 441

ISBN-13: 9783030258214

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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.

Effective Statistical Learning Methods for Actuaries
Effective Statistical Learning Methods for Actuaries

Author: Michel Denuit

Publisher:

Published: 2019

Total Pages:

ISBN-13: 9783030258283

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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.

Predictive Modeling Applications in Actuarial Science
Predictive Modeling Applications in Actuarial Science

Author: Edward W. Frees

Publisher: Cambridge University Press

Published: 2014-07-28

Total Pages: 565

ISBN-13: 1107029872

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This book is for actuaries and financial analysts developing their expertise in statistics and who wish to become familiar with concrete examples of predictive modeling.

Fundamentals of Actuarial Mathematics
Fundamentals of Actuarial Mathematics

Author: S. David Promislow

Publisher: John Wiley & Sons

Published: 2011-01-06

Total Pages: 390

ISBN-13: 0470978074

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This book provides a comprehensive introduction to actuarial mathematics, covering both deterministic and stochastic models of life contingencies, as well as more advanced topics such as risk theory, credibility theory and multi-state models. This new edition includes additional material on credibility theory, continuous time multi-state models, more complex types of contingent insurances, flexible contracts such as universal life, the risk measures VaR and TVaR. Key Features: Covers much of the syllabus material on the modeling examinations of the Society of Actuaries, Canadian Institute of Actuaries and the Casualty Actuarial Society. (SOA-CIA exams MLC and C, CSA exams 3L and 4.) Extensively revised and updated with new material. Orders the topics specifically to facilitate learning. Provides a streamlined approach to actuarial notation. Employs modern computational methods. Contains a variety of exercises, both computational and theoretical, together with answers, enabling use for self-study. An ideal text for students planning for a professional career as actuaries, providing a solid preparation for the modeling examinations of the major North American actuarial associations. Furthermore, this book is highly suitable reference for those wanting a sound introduction to the subject, and for those working in insurance, annuities and pensions.