- Preliminaries
- Preface
- Contributors
- Chapter 1 Introduction
- 1.1 R for Actuarial Science?
- 1.2 Importing and Creating Various Objects, and Datasets in R
- 1.3 Basics of the R Language
- 1.4 More Advanced R
- 1.5 Ending an R Session
- 1.6 Exercises
- Figure 1.1
- Table 1.1
- Part I Methodology
- Chapter 2 Standard Statistical Inference
- Chapter 3 Bayesian Philosophy
- Chapter 4 Statistical Learning
- Chapter 5 Spatial Analysis
- 5.1 Introduction
- 5.2 Spatial Analysis and GIS
- 5.3 Spatial Objects in R
- 5.4 Maps in R
- 5.5 Reading Maps and Data in R
- 5.6 Exploratory Spatial Data Analysis
- 5.7 Testing for Spatial Correlation
- 5.8 Spatial Car Accident Insurance Analysis
- 5.9 Spatial Car Accident Insurance Shared Analysis
- 5.10 Conclusion
- Figure 5.1
- Table 5.1
- Chapter 6 Reinsurance and Extremal Events
- Part II Life Insurance
- Chapter 7 Life Contingencies
- Chapter 8 Prospective Life Tables
- Chapter 9 Prospective Mortality Tables and Portfolio Experience
- 9.1 Introduction and Motivation
- 9.2 Notation, Data, and Assumption
- 9.3 The Methods
- 9.3.1 Method 1: Approach Involving One Parameter with the SMR
- 9.3.2 Method 2: Approach Involving Two Parameters with a Semiparametric Relational Model
- 9.3.3 Method 3: Poisson GLM Including Interactions with Age and Calendar Year
- 9.3.4 Method 4: Nonparametric Smoothing and Application of the Improvement Rates
- 9.3.5 Completion of the Tables: The Approach of Denuit and Goderniaux
- 9.4 Validation
- 9.5 Operational Framework
- Figure 9.1
- Table 9.1
- Chapter 10 Survival Analysis
- Part III Finance
- Chapter 11 Stock Prices and Time Series
- Chapter 12 Yield Curves and Interest Rates Models
- Chapter 13 Portfolio Allocation
- Part IV Non-Life Insurance
- Chapter 14 General Insurance Pricing
- 14.1 Introduction and Motivation
- 14.2 Claims Frequency and Log-Poisson Regression
- 14.3 From Poisson to Quasi-Poisson
- 14.4 More Advanced Models for Counts
- 14.5 Individual Claims, Gamma, Log-Normal, and Other Regressions
- 14.6 Large Claims and Ratemaking
- 14.7 Modeling Compound Sum with Tweedie Regression
- 14.8 Exercises
- Figure 14.1
- Chapter 15 Longitudinal Data and Experience Rating
- Chapter 16 Claims Reserving and IBNR
- Chapter 17 Bibliography
Contributors
Katrien Antonio
Universiteit van Amsterdam
Amsterdam, Netherlands
Renato Assunção
Universidade Federal de Minas Gerais
Belo Horizonte, Brazil
Frank van Berkum
Universiteit van Amsterdam
Amsterdam, Netherlands
Heather Booth
Australian National University
Canberra, Australia
Jean-Philippe Boucher
Université du Québec à Montréal
Montréal, Québec, Canada
Yohan Chalabi
ETH Zürich
Zürich, Switzerland
Arthur Charpentier
Université du Québec à Montréal
Montréal, Québec, Canada
Marcelo Azevedo Costa
Universidade Federal de Minas Gerais
Belo Horizonte, Brazil
Christophe Dutang
Université de Strasbourg & Université du Maine
Le Mans, France
Benedict Escoto
Aon Benfield
Chicago, Illinois, USA
Markus Gesmann
ChainLadder Project
London, United Kingdom
Leonie Tickle
Macquarie University
Sydney, Australia
Julien Tomas
Université de Lyon 1
Lyon, France
Eric Gilleland
National Center for Atmospheric Research
Boulder, Colorado, USA
Sergio S. Guirreri
Accenture S.p.A.
Milan, Italy
Rob J. Hyndman
Monash University
Melbourne, Australia
Rob Kaas
Universiteit van Amsterdam
Amsterdam, Netherlands
Frédéric Planchet
Université de Lyon 1
Lyon, France
Marcos Oliveira Prates
Universidade Federal de Minas Gerais
Belo Horizonte, Brazil
Mathieu Ribatet
Université de Montpellier 2
Montpellier, France
Peng Shi
University of Wisconsin-Madison
Madison, Wisconsin, USA
Luís Gustavo Silva e Silva
Universidade Federal de Minas Gerais
Belo Horizonte, Brazil
Giorgio Spedicato
UnipolSai Assicurazioni
Bologna, Italy
Pierre-E. Thérond
Université de Lyon 1
Lyon, France
Stéphane Tufféry
Ecole Nationale de la Statistique et
de l'Analyse de l'Information (ENSAI) &
Université de Rennes 1
Rennes, France
Diethelm Würtz
ETH Zürich
Zürich, Switzerland
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