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Cover: 9783031124112 | Statistical Foundations of Actuarial Learning and its Applications
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Statistical Foundations of Actuarial Learning and its Applications
Taschenbuch von Michael Merz (u. a.)
Sprache: Englisch

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Beschreibung
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.
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.
Über den Autor
¿Mario Wüthrich is Professor in the Department of Mathematics at ETH Zurich, Honorary Visiting Professor at City, University of London (2011-2022), Honorary Professor at University College London (2013-2019), and Adjunct Professor at University of Bologna (2014-2016). He holds a Ph.D. in Mathematics from ETH Zurich (1999). From 2000 to 2005, he held an actuarial position at Winterthur Insurance, Switzerland. He is Actuary SAA (2004), served on the board of the Swiss Association of Actuaries (2006-2018), and is Editor-in-Chief of ASTIN Bulletin (since 2018). Michael Merz has been the holder of the Chair of Mathematics and Statistics in Economics at the University of Hamburg since 2009. After completing his doctorate at the University of Tübingen on a topic from the field of risk theory, he worked from 2004 to 2006 in the actuarial department of Baloise Insurance Group and gained practical experience in the areas of quantitative risk management and Actuarial Science. He then worked until 2009 as a Juniorprofessor for statistics, risk and insurance at the University of Tübingen. Since the beginning of 2018 he is editor of ASTIN Bulletin.
Zusammenfassung

This book is open access, which means that you have free and unlimited access

Uniquely combines classical statistical modeling with modern machine learning methods

Discusses the state-of-the-art in predictive modeling for actuarial work

Provides machine learning and statistical tools with a focus on actuarial applications and their special features

Inhaltsverzeichnis
- 1. Introduction. - 2. Exponential Dispersion Family. - 3. Estimation Theory. - 4. Predictive Modeling and Forecast Evaluation. - 5. Generalized Linear Models. - 6. Bayesian Methods, Regularization and Expectation-Maximization. - 7. Deep Learning. - 8. Recurrent Neural Networks. - 9. Convolutional Neural Networks. - 10. Natural Language Processing. - 11. Selected Topics in Deep Learning. - 12. Appendix A: Technical Results on Networks. - 13. Appendix B: Data and Examples.
Details
Erscheinungsjahr: 2022
Fachbereich: Allgemeines
Genre: Mathematik, Medizin, Naturwissenschaften, Technik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Reihe: Springer Actuarial
Inhalt: XII
605 S.
1 s/w Illustr.
605 p. 1 illus.
ISBN-13: 9783031124112
ISBN-10: 3031124111
Sprache: Englisch
Herstellernummer: 978-3-031-12411-2
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Merz, Michael
Wüthrich, Mario V.
Auflage: 1st ed. 2023
Hersteller: Springer Nature Switzerland
Springer International Publishing
Springer International Publishing AG
Springer Actuarial
Verantwortliche Person für die EU: Books on Demand GmbH, In de Tarpen 42, D-22848 Norderstedt, info@bod.de
Maße: 235 x 155 x 34 mm
Von/Mit: Michael Merz (u. a.)
Erscheinungsdatum: 23.11.2022
Gewicht: 0,925 kg
Artikel-ID: 122044822
Über den Autor
¿Mario Wüthrich is Professor in the Department of Mathematics at ETH Zurich, Honorary Visiting Professor at City, University of London (2011-2022), Honorary Professor at University College London (2013-2019), and Adjunct Professor at University of Bologna (2014-2016). He holds a Ph.D. in Mathematics from ETH Zurich (1999). From 2000 to 2005, he held an actuarial position at Winterthur Insurance, Switzerland. He is Actuary SAA (2004), served on the board of the Swiss Association of Actuaries (2006-2018), and is Editor-in-Chief of ASTIN Bulletin (since 2018). Michael Merz has been the holder of the Chair of Mathematics and Statistics in Economics at the University of Hamburg since 2009. After completing his doctorate at the University of Tübingen on a topic from the field of risk theory, he worked from 2004 to 2006 in the actuarial department of Baloise Insurance Group and gained practical experience in the areas of quantitative risk management and Actuarial Science. He then worked until 2009 as a Juniorprofessor for statistics, risk and insurance at the University of Tübingen. Since the beginning of 2018 he is editor of ASTIN Bulletin.
Zusammenfassung

This book is open access, which means that you have free and unlimited access

Uniquely combines classical statistical modeling with modern machine learning methods

Discusses the state-of-the-art in predictive modeling for actuarial work

Provides machine learning and statistical tools with a focus on actuarial applications and their special features

Inhaltsverzeichnis
- 1. Introduction. - 2. Exponential Dispersion Family. - 3. Estimation Theory. - 4. Predictive Modeling and Forecast Evaluation. - 5. Generalized Linear Models. - 6. Bayesian Methods, Regularization and Expectation-Maximization. - 7. Deep Learning. - 8. Recurrent Neural Networks. - 9. Convolutional Neural Networks. - 10. Natural Language Processing. - 11. Selected Topics in Deep Learning. - 12. Appendix A: Technical Results on Networks. - 13. Appendix B: Data and Examples.
Details
Erscheinungsjahr: 2022
Fachbereich: Allgemeines
Genre: Mathematik, Medizin, Naturwissenschaften, Technik
Rubrik: Naturwissenschaften & Technik
Medium: Taschenbuch
Reihe: Springer Actuarial
Inhalt: XII
605 S.
1 s/w Illustr.
605 p. 1 illus.
ISBN-13: 9783031124112
ISBN-10: 3031124111
Sprache: Englisch
Herstellernummer: 978-3-031-12411-2
Ausstattung / Beilage: Paperback
Einband: Kartoniert / Broschiert
Autor: Merz, Michael
Wüthrich, Mario V.
Auflage: 1st ed. 2023
Hersteller: Springer Nature Switzerland
Springer International Publishing
Springer International Publishing AG
Springer Actuarial
Verantwortliche Person für die EU: Books on Demand GmbH, In de Tarpen 42, D-22848 Norderstedt, info@bod.de
Maße: 235 x 155 x 34 mm
Von/Mit: Michael Merz (u. a.)
Erscheinungsdatum: 23.11.2022
Gewicht: 0,925 kg
Artikel-ID: 122044822
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