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   系統號碼830152
   書刊名Effective statistical learning methods for actuaries I [electronic resource] : GLMs and extensions /
   主要著者Denuit, Michel.
   其他著者Hainaut, Donatien.;Trufin, Julien.
   出版項Cham : Imprint: Springer, 2019.
   索書號HG8781.D45 2019
   ISBN9783030258207
   標題Actuarial science.
Insurance-Statistical methods.
Linear models (Statistics)
Actuarial Sciences.
Statistics for Business, Management, Economics, Finance, Insurance.
   電子資源https://doi.org/10.1007/978-3-030-25820-7
   叢書名Springer actuarial,2523-3270
   
    
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內容簡介This 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 insurance data analytics with applications to P&C, life and health insurance. Although closely related to the other two volumes, this volume can be read independently.

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