id |
oapen-20.500.12657-60157
|
record_format |
dspace
|
spelling |
oapen-20.500.12657-601572024-03-27T14:14:53Z Statistical Foundations of Actuarial Learning and its Applications Wüthrich, Mario V. Merz, Michael Deep Learning Actuarial Modeling Pricing and Claims Reserving Artificial Neural Networks Regression Modeling thema EDItEUR::P Mathematics and Science::PB Mathematics::PBW Applied mathematics thema EDItEUR::P Mathematics and Science::PB Mathematics::PBT Probability and statistics thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence::UYQM Machine learning thema EDItEUR::U Computing and Information Technology::UM Computer programming / software engineering::UMB Algorithms and data structures thema EDItEUR::U Computing and Information Technology::UY Computer science::UYQ Artificial intelligence 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. 2022-12-13T12:36:08Z 2022-12-13T12:36:08Z 2023 book ONIX_20221213_9783031124099_24 9783031124099 https://library.oapen.org/handle/20.500.12657/60157 eng Springer Actuarial application/pdf n/a 978-3-031-12409-9.pdf https://link.springer.com/978-3-031-12409-9 Springer Nature Springer 10.1007/978-3-031-12409-9 10.1007/978-3-031-12409-9 6c6992af-b843-4f46-859c-f6e9998e40d5 5f350267-3ec9-4ceb-a365-7b7a7bb0bd24 f6a2a9bb-8c8e-4665-8a6f-3c889b57693d 9783031124099 Springer 605 Cham [...] [...] Swiss Re open access
|
institution |
OAPEN
|
collection |
DSpace
|
language |
English
|
description |
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.
|
title |
978-3-031-12409-9.pdf
|
spellingShingle |
978-3-031-12409-9.pdf
|
title_short |
978-3-031-12409-9.pdf
|
title_full |
978-3-031-12409-9.pdf
|
title_fullStr |
978-3-031-12409-9.pdf
|
title_full_unstemmed |
978-3-031-12409-9.pdf
|
title_sort |
978-3-031-12409-9.pdf
|
publisher |
Springer Nature
|
publishDate |
2022
|
url |
https://link.springer.com/978-3-031-12409-9
|
_version_ |
1799945263243067392
|