Computational Actuarial Science with R
eBook - ePub

Computational Actuarial Science with R

  1. 656 pages
  2. English
  3. ePUB (mobile friendly)
  4. Available on iOS & Android
eBook - ePub

Computational Actuarial Science with R

About this book

A Hands-On Approach to Understanding and Using Actuarial ModelsComputational Actuarial Science with R provides an introduction to the computational aspects of actuarial science. Using simple R code, the book helps you understand the algorithms involved in actuarial computations. It also covers more advanced topics, such as parallel computing and C/

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Yes, you can access Computational Actuarial Science with R by Arthur Charpentier in PDF and/or ePUB format, as well as other popular books in Business & Finance. We have over one million books available in our catalogue for you to explore.

Information

Publisher
Chapman and Hall/CRC
Year
2014
Print ISBN
9781138033788
eBook ISBN
9781498759823
Topic
Business
Subtopic
Finance
Index
Business
Chapter 1

Introduction

Arthur Charpentier
Université du Québec à Montréal
Montréal, Québec, Canada
Rob Kaas
Amsterdam School of Economics, Universiteit van Amsterdam
Amsterdam, Netherlands

1.1 R for Actuarial Science?

As claimed on the CRAN website, http://cran.r-project.org/, R is an “open source software package, licensed under the GNU General Public License” (the so-called GPL). This simply means that R can be installed for free on most desktop and server machines. This platform independence and the open-source philosophy make R an ideal environment for reproducible research.
Why should students or researchers in actuarial science, or actuaries, use R for computations? Of primary interest, as suggested by Daryl Pregibon, research scientist at Google— quoted in Vance (2009)—is that R “allows statisticians to do very intricate and complicated analyses without knowing the blood and guts of computing systems.”
In this chapter, we will briefly introduce R, compare it with other standard programming languages, explain how to link R with them (if necessary), give an overview of the language, and show how to produce graphs. But as stated in Knuth (1973), “premature optimization is the root of all evil (or at least most of it) in programmin...

Table of contents

  1. Preliminaries
  2. Preface
  3. Contributors
  4. Chapter 1 Introduction
  5. Part I Methodology
  6. Chapter 2 Standard Statistical Inference
  7. Chapter 3 Bayesian Philosophy
  8. Chapter 4 Statistical Learning
  9. Chapter 5 Spatial Analysis
  10. Chapter 6 Reinsurance and Extremal Events
  11. Part II Life Insurance
  12. Chapter 7 Life Contingencies
  13. Chapter 8 Prospective Life Tables
  14. Chapter 9 Prospective Mortality Tables and Portfolio Experience
  15. Chapter 10 Survival Analysis
  16. Part III Finance
  17. Chapter 11 Stock Prices and Time Series
  18. Chapter 12 Yield Curves and Interest Rates Models
  19. Chapter 13 Portfolio Allocation
  20. Part IV Non-Life Insurance
  21. Chapter 14 General Insurance Pricing
  22. Chapter 15 Longitudinal Data and Experience Rating
  23. Chapter 16 Claims Reserving and IBNR
  24. Chapter 17 Bibliography