Overview
- 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
Part of the book series: Springer Actuarial (SPACT)
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About this book
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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Keywords
Table of contents (13 chapters)
Authors and Affiliations
About the authors
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.
Bibliographic Information
Book Title: Statistical Foundations of Actuarial Learning and its Applications
Authors: Mario V. Wüthrich, Michael Merz
Series Title: Springer Actuarial
DOI: https://doi.org/10.1007/978-3-031-12409-9
Publisher: Springer Cham
eBook Packages: Mathematics and Statistics, Mathematics and Statistics (R0)
Copyright Information: The Authors 2023
Hardcover ISBN: 978-3-031-12408-2Published: 23 November 2022
Softcover ISBN: 978-3-031-12411-2Published: 23 November 2022
eBook ISBN: 978-3-031-12409-9Published: 22 November 2022
Series ISSN: 2523-3262
Series E-ISSN: 2523-3270
Edition Number: 1
Number of Pages: XII, 605
Number of Illustrations: 1 b/w illustrations
Topics: Applications of Mathematics, Statistics for Business, Management, Economics, Finance, Insurance, Machine Learning, Data Structures and Information Theory, Artificial Intelligence