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001 978-3-031-12409-9
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005 20240508090327.0
007 cr nn 008mamaa
008 221122s2023 sz | s |||| 0|eng d
020 _a9783031124099
_9978-3-031-12409-9
024 7 _a10.1007/978-3-031-12409-9
_2doi
050 4 _aHG8779-8793
072 7 _aKFFN
_2bicssc
072 7 _aMAT003000
_2bisacsh
072 7 _aKFFN
_2thema
082 0 4 _a368.01
_223
100 1 _aWüthrich, Mario V.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
245 1 0 _aStatistical Foundations of Actuarial Learning and its Applications
_h[electronic resource] /
_cby Mario V. Wüthrich, Michael Merz.
250 _a1st ed. 2023.
264 1 _aCham :
_bSpringer International Publishing :
_bImprint: Springer,
_c2023.
300 _aXII, 605 p. 1 illus.
_bonline resource.
336 _atext
_btxt
_2rdacontent
337 _acomputer
_bc
_2rdamedia
338 _aonline resource
_bcr
_2rdacarrier
347 _atext file
_bPDF
_2rda
490 1 _aSpringer Actuarial,
_x2523-3270
506 0 _aOpen Access
520 _aThis 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.
650 0 _aActuarial science.
650 0 _aStatistics .
650 0 _aMachine learning.
650 0 _aArtificial intelligence
_xData processing.
650 0 _aSocial sciences
_xMathematics.
650 1 4 _aActuarial Mathematics.
650 2 4 _aStatistics in Business, Management, Economics, Finance, Insurance.
650 2 4 _aMachine Learning.
650 2 4 _aData Science.
650 2 4 _aMathematics in Business, Economics and Finance.
700 1 _aMerz, Michael.
_eauthor.
_4aut
_4http://id.loc.gov/vocabulary/relators/aut
710 2 _aSpringerLink (Online service)
773 0 _tSpringer Nature eBook
776 0 8 _iPrinted edition:
_z9783031124082
776 0 8 _iPrinted edition:
_z9783031124105
776 0 8 _iPrinted edition:
_z9783031124112
830 0 _aSpringer Actuarial,
_x2523-3270
856 4 0 _uhttps://doi.org/10.1007/978-3-031-12409-9
912 _aZDB-2-SMA
912 _aZDB-2-SXMS
912 _aZDB-2-SOB
999 _c37768
_d37768