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Effective Statistical Learning Methods for Actuaries I

GLMs and Extensions

Language EnglishEnglish
Book Paperback
Book Effective Statistical Learning Methods for Actuaries I Michel Denuit
Libristo code: 24519041
Publishers Springer Nature Switzerland AG, September 2019
This book summarizes the state of the art in generalized linear models (GLMs) and their various exte... Full description
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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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About the book

Full name Effective Statistical Learning Methods for Actuaries I
Language English
Binding Book - Paperback
Date of issue 2019
Number of pages 441
EAN 9783030258191
Libristo code 24519041
Weight 708
Dimensions 238 x 159 x 27
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