000 | 03347nam a22006015i 4500 | ||
---|---|---|---|
001 | 978-3-031-12409-9 | ||
003 | DE-He213 | ||
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 |
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_aMAT003000 _2bisacsh |
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_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 |
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337 |
_acomputer _bc _2rdamedia |
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338 |
_aonline resource _bcr _2rdacarrier |
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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 |