Book description
This book demystifies the computational aspects of actuarial science, showing that even complex computations can usually be done without too much trouble. Using simple R code, the book helps readers understand the algorithms involved in actuarial computations. It also covers more advanced topics, such as parallel computing and C/C++ embedded codes. Datasets used in the text are available in an R package (CASdatasets).
Table of contents
- 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
- 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
- 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
- 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
- Chapter 15 Longitudinal Data and Experience Rating
- Chapter 16 Claims Reserving and IBNR
- Chapter 17 Bibliography
Product information
- Title: Computational Actuarial Science with R
- Author(s):
- Release date: August 2014
- Publisher(s): Chapman and Hall/CRC
- ISBN: 9781498759823
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