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Sinopsis
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.
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Acerca del 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.
De la contraportada
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 tointerpret 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.
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Statistical Foundations of Actuarial Learning and its Applications (Springer Actuarial)
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Statistical Foundations of Actuarial Learning and its Applications
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Springer International Publishing Nov 2022, 2022
ISBN 10: 3031124081
ISBN 13: 9783031124082
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Buch. Condición: Neu. This item is printed on demand - it takes 3-4 days longer - Neuware -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. 620 pp. Englisch. Nº de ref. del artículo: 9783031124082
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Statistical Foundations of Actuarial Learning and its Applications
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Springer International Publishing AG, CH, 2022
ISBN 10: 3031124081
ISBN 13: 9783031124082
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Librería: Rarewaves.com UK, London, Reino Unido
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Hardback. Condición: New. 1st ed. 2023. 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. Nº de ref. del artículo: LU-9783031124082
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Statistical Foundations of Actuarial Learning and Its Applications
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Statistical Foundations of Actuarial Learning and its Applications
Publicado por
Springer International Publishing AG, CH, 2022
ISBN 10: 3031124081
ISBN 13: 9783031124082
Nuevo
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Librería: Rarewaves.com USA, London, LONDO, Reino Unido
Calificación del vendedor: 5 de 5 estrellas
Hardback. Condición: New. 1st ed. 2023. 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. Nº de ref. del artículo: LU-9783031124082
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Statistical Foundations of Actuarial Learning and Its Applications
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Statistical Foundations of Actuarial Learning and Its Applications
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Statistical Foundations of Actuarial Learning and Its Applications
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Statistical Foundations of Actuarial Learning and its Applications
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ISBN 10: 3031124081
ISBN 13: 9783031124082
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Librería: AHA-BUCH GmbH, Einbeck, Alemania
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Buch. Condición: Neu. Druck auf Anfrage Neuware - Printed after ordering - 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. Nº de ref. del artículo: 9783031124082
Cantidad disponible: 1 disponibles
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Statistical Foundations of Actuarial Learning and its Applications
Publicado por
Springer International Publishing, 2022
ISBN 10: 3031124081
ISBN 13: 9783031124082
Nuevo
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Librería: moluna, Greven, Alemania
Calificación del vendedor: 5 de 5 estrellas
Condición: New. Dieser Artikel ist ein Print on Demand Artikel und wird nach Ihrer Bestellung fuer Sie gedruckt. This book is open access, which means that you have free and unlimited accessUniquely combines classical statistical modeling with modern machine learning methodsDiscusses the state-of-the-art in predictive modeling for actuarial work. Nº de ref. del artículo: 668447237
Cantidad disponible: Más de 20 disponibles