Effective Statistical Learning Methods For Actuaries I
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Effective Statistical Learning Methods For Actuaries I
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Author : Michel Denuit
language : en
Publisher: Springer Nature
Release Date : 2019-09-03
Effective Statistical Learning Methods For Actuaries I written by Michel Denuit and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-09-03 with Business & Economics categories.
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 Iii
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Author : Michel Denuit
language : en
Publisher: Springer Nature
Release Date : 2019-10-31
Effective Statistical Learning Methods For Actuaries Iii written by Michel Denuit and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-10-31 with Business & Economics categories.
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 Ii
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Author : Michel Denuit
language : en
Publisher: Springer Nature
Release Date : 2020-11-16
Effective Statistical Learning Methods For Actuaries Ii written by Michel Denuit and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2020-11-16 with Business & Economics categories.
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
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Author : Michel Denuit
language : en
Publisher:
Release Date : 2019
Effective Statistical Learning Methods For Actuaries written by Michel Denuit and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Actuarial science categories.
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 I
DOWNLOAD
Author : Michel Denuit
language : en
Publisher:
Release Date : 2019
Effective Statistical Learning Methods For Actuaries I written by Michel Denuit and has been published by this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019 with Actuarial science categories.
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
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Author : Mario V. Wüthrich
language : en
Publisher: Springer Nature
Release Date : 2022-11-22
Statistical Foundations Of Actuarial Learning And Its Applications written by Mario V. Wüthrich and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-11-22 with Mathematics categories.
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.
Statistical And Probabilistic Methods In Actuarial Science
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Author : Philip J. Boland
language : en
Publisher: CRC Press
Release Date : 2007-03-05
Statistical And Probabilistic Methods In Actuarial Science written by Philip J. Boland and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2007-03-05 with Business & Economics categories.
Statistical and Probabilistic Methods in Actuarial Science covers many of the diverse methods in applied probability and statistics for students aspiring to careers in insurance, actuarial science, and finance. The book builds on students' existing knowledge of probability and statistics by establishing a solid and thorough understanding of
Mathematical And Statistical Methods For Actuarial Sciences And Finance
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Author : Marco Corazza
language : en
Publisher: Springer Nature
Release Date : 2021-12-13
Mathematical And Statistical Methods For Actuarial Sciences And Finance written by Marco Corazza and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-12-13 with Business & Economics categories.
The cooperation and contamination between mathematicians, statisticians and econometricians working in actuarial sciences and finance is improving the research on these topics and producing numerous meaningful scientific results. This volume presents new ideas, in the form of four- to six-page papers, presented at the International Conference eMAF2020 – Mathematical and Statistical Methods for Actuarial Sciences and Finance. Due to the now sadly famous COVID-19 pandemic, the conference was held remotely through the Zoom platform offered by the Department of Economics of the Ca’ Foscari University of Venice on September 18, 22 and 25, 2020. eMAF2020 is the ninth edition of an international biennial series of scientific meetings, started in 2004 at the initiative of the Department of Economics and Statistics of the University of Salerno. The effectiveness of this idea has been proven by wide participation in all editions, which have been held in Salerno (2004, 2006, 2010 and 2014), Venice (2008, 2012 and 2020), Paris (2016) and Madrid (2018). This book covers a wide variety of subjects: artificial intelligence and machine learning in finance and insurance, behavioral finance, credit risk methods and models, dynamic optimization in finance, financial data analytics, forecasting dynamics of actuarial and financial phenomena, foreign exchange markets, insurance models, interest rate models, longevity risk, models and methods for financial time series analysis, multivariate techniques for financial markets analysis, pension systems, portfolio selection and management, real-world finance, risk analysis and management, trading systems, and others. This volume is a valuable resource for academics, PhD students, practitioners, professionals and researchers. Moreover, it is also of interest to other readers with quantitative background knowledge.
Predictive Modeling Applications In Actuarial Science
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Author : Edward W. Frees
language : en
Publisher: Cambridge University Press
Release Date : 2014-07-28
Predictive Modeling Applications In Actuarial Science written by Edward W. Frees and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014-07-28 with Business & Economics categories.
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.
Mathematical And Statistical Methods For Actuarial Sciences And Finance
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Author : Marco Corazza
language : en
Publisher: Springer Nature
Release Date : 2022-04-11
Mathematical And Statistical Methods For Actuarial Sciences And Finance written by Marco Corazza and has been published by Springer Nature this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-04-11 with Mathematics categories.
The cooperation and contamination among mathematicians, statisticians and econometricians working in actuarial sciences and finance are improving the research on these topics and producing numerous meaningful scientific results. This volume presents new ideas in the form of four- to six-page papers presented at the International Conference MAF2022 – Mathematical and Statistical Methods for Actuarial Sciences and Finance. Due to the COVID-19 pandemic, the conference, to which this book is related, was organized in a hybrid form by the Department of Economics and Statistics of the University of Salerno, with the partnership of the Department of Economics of Cà Foscari University of Venice, and was held from 20 to 22 April 2022 in Salerno (Italy) MAF2022 is the tenth edition of an international biennial series of scientific meetings, started in 2004 on the initiative of the Department of Economics and Statistics of the University of Salerno. It has established itself internationally with gradual and continuous growth and scientific enrichment. The effectiveness of this idea has been proven by the wide participation in all the editions, which have been held in Salerno (2004, 2006, 2010, 2014, 2022), Venice (2008, 2012 and 2020 online), Paris (2016) and Madrid (2018). This book covers a wide variety of subjects: artificial intelligence and machine learning in finance and insurance, behavioural finance, credit risk methods and models, dynamic optimization in finance, financial data analytics, forecasting dynamics of actuarial and financial phenomena, foreign exchange markets, insurance models, interest rate models, longevity risk, models and methods for financial time series analysis, multivariate techniques for financial markets analysis, pension systems, portfolio selection and management, real-world finance, risk analysis and management, trading systems, and others. This volume is a valuable resource for academics, PhD students, practitioners, professionals and researchers. Moreover, it is also of interest to other readers with quantitative background knowledge.