LEADER 03903nam 22007935 450 001 9910632470503321 005 20250628110048.0 010 $a3-031-12409-X 024 7 $a10.1007/978-3-031-12409-9 035 $a(CKB)5580000000468789 035 $a(DE-He213)978-3-031-12409-9 035 $a(oapen)https://directory.doabooks.org/handle/20.500.12854/94965 035 $a(MiAaPQ)EBC7144518 035 $a(Au-PeEL)EBL7144518 035 $a(OCoLC)1356008648 035 $a(PPN)266352626 035 $a(ODN)ODN0010073748 035 $a(EXLCZ)995580000000468789 100 $a20221122d2023 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aStatistical Foundations of Actuarial Learning and its Applications /$fby Mario V. Wüthrich, Michael Merz 205 $a1st ed. 2023. 210 $aCham$cSpringer Nature$d2023 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2023. 215 $a1 online resource (XII, 605 p. 1 illus.) 225 1 $aSpringer Actuarial,$x2523-3270 311 08$a3-031-12408-1 330 $aThis open access book discusses the statistical modeling of insurance problems, a process which comprises data collection, data analysis and statistical model building to forecast insured events that may happen in the future. It presents the mathematical foundations behind these fundamental statistical concepts and how they can be applied in daily actuarial practice. Statistical modeling has a wide range of applications, and, depending on the application, the theoretical aspects may be weighted differently: here the main focus is on prediction rather than explanation. Starting with a presentation of state-of-the-art actuarial models, such as generalized linear models, the book then dives into modern machine learning tools such as neural networks and text recognition to improve predictive modeling with complex features. Providing practitioners with detailed guidance on how to apply machine learning methods to real-world data sets, and how to interpret the results without losing sight of the mathematical assumptions on which these methods are based, the book can serve as a modern basis for an actuarial education syllabus. 410 0$aSpringer Actuarial,$x2523-3270 606 $aActuarial science 606 $aStatistics 606 $aMachine learning 606 $aArtificial intelligence?Data processing 606 $aSocial sciences?Mathematics 606 $aActuarial Mathematics 606 $aStatistics in Business, Management, Economics, Finance, Insurance 606 $aMachine Learning 606 $aData Science 606 $aMathematics in Business, Economics and Finance 606 $aAssegurances$2thub 606 $aEstadística$2thub 608 $aLlibres electrònics$2thub 615 0$aActuarial science. 615 0$aStatistics. 615 0$aMachine learning. 615 0$aArtificial intelligence?Data processing. 615 0$aSocial sciences?Mathematics. 615 14$aActuarial Mathematics. 615 24$aStatistics in Business, Management, Economics, Finance, Insurance. 615 24$aMachine Learning. 615 24$aData Science. 615 24$aMathematics in Business, Economics and Finance. 615 7$aAssegurances. 615 7$aEstadística 676 $a368.01 686 $aBUS061000$aCOM004000$aCOM031000$aMAT003000$2bisacsh 700 $aWu?thrich$b Mario V.$4aut$4http://id.loc.gov/vocabulary/relators/aut$00 702 $aMerz$b Michael$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910632470503321 996 $aStatistical Foundations of Actuarial Learning and its Applications$92996023 997 $aUNINA