LEADER 03760nam 22006015 450 001 9910349321203321 005 20251113174316.0 010 $a9783030258207 010 $a3030258203 024 7 $a10.1007/978-3-030-25820-7 035 $a(CKB)4100000009191103 035 $a(DE-He213)978-3-030-25820-7 035 $a(MiAaPQ)EBC5924586 035 $a(PPN)258306394 035 $a(MiAaPQ)EBC31862526 035 $a(Au-PeEL)EBL31862526 035 $a(EXLCZ)994100000009191103 100 $a20190903d2019 u| 0 101 0 $aeng 135 $aurnn#008mamaa 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aEffective Statistical Learning Methods for Actuaries I $eGLMs and Extensions /$fby Michel Denuit, Donatien Hainaut, Julien Trufin 205 $a1st ed. 2019. 210 1$aCham :$cSpringer International Publishing :$cImprint: Springer,$d2019. 215 $a1 online resource (XVI, 441 p. 82 illus., 23 illus. in color.) 225 1 $aSpringer Actuarial Lecture Notes,$x2523-3297 311 08$a9783030258191 311 08$a303025819X 327 $aPreface -- Part I: LOSS MODELS.-1. Insurance Risk Classification.-Exponential Dispersion (ED) Distributions.-3.-Maximum Likelihood Estimation.-Part II LINEAR MODELS.-4. Generalized Linear Models (GLMs) -- 5.-Over-dispersion, credibility adjustments, mixed models, and regularization.-Part III ADDITIVE MODELS -- 6 Generalized Additive Models (GAMs) -- 7. Beyond Mean Modeling: Double GLMs and GAMs for Location, Scale and Shape (GAMLSS) -- Part IV SPECIAL TOPICS -- 8. Some Generalized Non-Linear Models (GNMs) -- 9 Extreme Value Models -- References. 330 $aThis book summarizes the state of the art in generalized linear models (GLMs) and their various extensions: GAMs, mixed models and credibility, and some nonlinear variants (GNMs). In order to deal with tail events, analytical tools from Extreme Value Theory are presented. Going beyond mean modeling, it considers volatility modeling (double GLMs) and the general modeling of location, scale and shape parameters (GAMLSS). Actuaries need these advanced analytical tools to turn the massive data sets now at their disposal into opportunities. The exposition alternates between methodological aspects and case studies, providing numerical illustrations using the R statistical software. The technical prerequisites are kept at a reasonable level in order to reach a broad readership. This is the first of three volumes entitled Effective Statistical Learning Methods for Actuaries. Written by actuaries for actuaries, this series offers a comprehensive overview of insurancedata analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently. 410 0$aSpringer Actuarial Lecture Notes,$x2523-3297 606 $aActuarial science 606 $aStatistics 606 $aActuarial Mathematics 606 $aStatistics in Business, Management, Economics, Finance, Insurance 615 0$aActuarial science. 615 0$aStatistics. 615 14$aActuarial Mathematics. 615 24$aStatistics in Business, Management, Economics, Finance, Insurance. 676 $a368.01 676 $a368.01 700 $aDenuit$b Michel$4aut$4http://id.loc.gov/vocabulary/relators/aut$0781288 702 $aHainaut$b Donatien$4aut$4http://id.loc.gov/vocabulary/relators/aut 702 $aTrufin$b Julien$4aut$4http://id.loc.gov/vocabulary/relators/aut 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910349321203321 996 $aEffective Statistical Learning Methods for Actuaries I$92541023 997 $aUNINA