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Effective Statistical Learning Methods for Actuaries I

Effective Statistical Learning Methods for Actuaries I PDF Author: Michel Denuit
Publisher: Springer Nature
ISBN: 3030258203
Category : Business & Economics
Languages : en
Pages : 452

Book Description
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 PDF Author: Michel Denuit
Publisher: Springer Nature
ISBN: 3030258203
Category : Business & Economics
Languages : en
Pages : 452

Book Description
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 II

Effective Statistical Learning Methods for Actuaries II PDF Author: Michel Denuit
Publisher: Springer Nature
ISBN: 303057556X
Category : Business & Economics
Languages : en
Pages : 235

Book Description
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

Effective Statistical Learning Methods for Actuaries PDF Author: Michel Denuit
Publisher:
ISBN: 9783030258283
Category : Actuarial science
Languages : en
Pages :

Book Description
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.

Effective Statistical Learning Methods for Actuaries III

Effective Statistical Learning Methods for Actuaries III PDF Author: Michel Denuit
Publisher: Springer
ISBN: 9783030258269
Category : Business & Economics
Languages : en
Pages : 250

Book Description
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.

Effective Statistical Learning Methods for Actuaries I

Effective Statistical Learning Methods for Actuaries I PDF Author: Michel Denuit
Publisher:
ISBN: 9783030258214
Category : Actuarial science
Languages : en
Pages : 441

Book Description
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.

Statistical Foundations of Actuarial Learning and its Applications

Statistical Foundations of Actuarial Learning and its Applications PDF Author: Mario V. Wüthrich
Publisher: Springer Nature
ISBN: 303112409X
Category : Mathematics
Languages : en
Pages : 611

Book Description
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.

Regression Modeling with Actuarial and Financial Applications

Regression Modeling with Actuarial and Financial Applications PDF Author: Edward W. Frees
Publisher: Cambridge University Press
ISBN: 0521760119
Category : Business & Economics
Languages : en
Pages : 585

Book Description
This book teaches multiple regression and time series and how to use these to analyze real data in risk management and finance.

Pricing Insurance Risk

Pricing Insurance Risk PDF Author: Stephen J. Mildenhall
Publisher: John Wiley & Sons
ISBN: 1119755670
Category : Business & Economics
Languages : en
Pages : 564

Book Description
PRICING INSURANCE RISK A comprehensive framework for measuring, valuing, and managing risk Pricing 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 optimization Perfect 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.

Predictive Modeling Applications in Actuarial Science

Predictive Modeling Applications in Actuarial Science PDF Author: Edward W. Frees
Publisher: Cambridge University Press
ISBN: 1107029872
Category : Business & Economics
Languages : en
Pages : 565

Book Description
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.

Computational Actuarial Science with R

Computational Actuarial Science with R PDF Author: Arthur Charpentier
Publisher: CRC Press
ISBN: 1498759823
Category : Business & Economics
Languages : en
Pages : 652

Book Description
A Hands-On Approach to Understanding and Using Actuarial ModelsComputational Actuarial Science with R provides an introduction to the computational aspects of actuarial science. Using simple R code, the book helps you understand the algorithms involved in actuarial computations. It also covers more advanced topics, such as parallel computing and C/