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Effective Statistical Learning Methods for Actuaries I

GLMs and Extensions

  • Michel Denuit
  • Donatien Hainaut
  • Julien Trufin

Part of the Springer Actuarial book series (SPACT)

Also part of the Springer Actuarial Lecture Notes book sub series (SPACLN)

Table of contents

  1. Front Matter
    Pages i-xvi
  2. Loss Models

    1. Front Matter
      Pages 1-1
    2. Michel Denuit, Donatien Hainaut, Julien Trufin
      Pages 3-26
    3. Michel Denuit, Donatien Hainaut, Julien Trufin
      Pages 27-68
    4. Michel Denuit, Donatien Hainaut, Julien Trufin
      Pages 69-94
  3. Linear Models

    1. Front Matter
      Pages 95-95
    2. Michel Denuit, Donatien Hainaut, Julien Trufin
      Pages 97-196
    3. Michel Denuit, Donatien Hainaut, Julien Trufin
      Pages 197-250
  4. Additive Models

    1. Front Matter
      Pages 251-251
    2. Michel Denuit, Donatien Hainaut, Julien Trufin
      Pages 253-327
    3. Michel Denuit, Donatien Hainaut, Julien Trufin
      Pages 329-359
  5. Special Topics

    1. Front Matter
      Pages 361-361
    2. Michel Denuit, Donatien Hainaut, Julien Trufin
      Pages 363-400
    3. Michel Denuit, Donatien Hainaut, Julien Trufin
      Pages 401-441

About this book

Introduction

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.

Keywords

Insurance risk classification Supervised learning Exponential dispersion model Regression analysis GLM

Authors and affiliations

  • Michel Denuit
    • 1
  • Donatien Hainaut
    • 2
  • Julien Trufin
    • 3
  1. 1.Institut de Statistique, Biostatistique et Sciences ActuariellesUniversité Catholique de LouvainLouvain-la-NeuveBelgium
  2. 2.Institut de Statistique, Biostatistique et Sciences ActuariellesUniversité Catholique de LouvainLouvain-la-NeuveBelgium
  3. 3.Département de MathématiquesUniversité Libre de BruxellesBrusselsBelgium

Bibliographic information

  • DOI https://doi.org/10.1007/978-3-030-25820-7
  • Copyright Information Springer Nature Switzerland AG 2019
  • Publisher Name Springer, Cham
  • eBook Packages Mathematics and Statistics
  • Print ISBN 978-3-030-25819-1
  • Online ISBN 978-3-030-25820-7
  • Series Print ISSN 2523-3262
  • Series Online ISSN 2523-3270
  • Buy this book on publisher's site