| Description |
1 online resource (xii, 605 pages) : illustrations. |
| Series |
Springer actuarial, 2523-3270 |
|
Springer actuarial. 2523-3270
|
| Access |
Open access GW5XE |
| Bibliography |
Includes bibliographical references and index. |
| Summary |
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. |
| Note |
Online resource; title from PDF title page (SpringerLink, viewed December 20, 2022). |
| Language |
English. |
| Local Note |
Springer Nature Springer Nature - SpringerLink eBooks - Fully Open Access |
| Subject |
Insurance -- Statistical methods.
|
|
Insurance -- Statistical methods
|
| Indexed Term |
Deep Learning |
|
Actuarial Modeling |
|
Pricing and Claims Reserving |
|
Artificial Neural Networks |
|
Regression Modeling |
| Added Author |
Merz, Michael, author.
|
| Other Form: |
Print version: Wüthrich, Mario V. Statistical Foundations of Actuarial Learning and Its Applications Cham : Springer International Publishing AG,c2022 9783031124082 |
| ISBN |
9783031124099 (electronic bk.) |
|
303112409X (electronic bk.) |
|
9783031124082 |
|
3031124111 |
|
9783031124112 |
| Standard No. |
10.1007/978-3-031-12409-9 doi |
|
