Effective Statistical Learning Methods for Actuaries I: GLMs and Extensions (Springer Actuarial) (Springer Actuarial Lecture Notes)

by Michel Denuit, Donatien Hainaut, and Julien Trufin

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Book cover for Effective Statistical Learning Methods for Actuaries I

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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.


  • ISBN13 9783030258191
  • Publish Date 18 September 2019
  • Publish Status Active
  • Publish Country CH
  • Imprint Springer Nature Switzerland AG
  • Edition 1st ed. 2019
  • Format Paperback
  • Pages 441
  • Language English