|aComputational actuarial science with R /|cedited by Arthur Charpentier.
|aBoca Raton :|bCRC Press,|cc2015.
|aBoca Raton :|bCRC Press,|c
|axxxi, 618 pages :|billustrations ;|c26 cm.
|aChapman & Hall/CRC the R series
|aIncludes bibliographical references (pages 583-604) and index.>505 0 1. Introduction / Arthur Charpentier and Rob Kaas -- I. Methodology -- 2. Standard statistical inference / Christophe Dutang -- 3. Bayesian philosophy / Benedict Escoto and Arthur Charpentier -- 4. Statistical learning / Arthur Charpentier and Stéphane Tufféry -- 5. Spatial analysis / Renato Assunção, Marcelo Azevedo Costa, Marcos Oliveira Prates, and Luís Gustavo Silva e Silva -- 6. Reinsurance and extremal events / Eric Gilleland and Mathieu Ribatet -- II. Life insurance -- 7. Life contingencies / Giorgio Spedicato -- 8. Prospective life tables / Heather Booth, Rob J. Hyndman, and Leonie Tickle -- 9. Prospective mortality tables and portfolio experience / Julien Tomas and Frédéric Planchet -- 10. Survival analysis / Frédéric Planchet and Pierre-E. Thérond -- III. Finance -- 11. Stock prices and time series / Yohan Chalabi and Diethelm Würtz -- 12. Yield curves and interest rates models / Sergio S. Guirreri -- 13. Portfolio allocation / Yohan Chalabi and Diethelm Würtz -- IV. Non-life insurance -- 14. General insurance pricing / Jean-Philippe Boucher and Arthur Charpentier -- 15. Longitudinal data and experience rating / Katrien Antonio, Peng Shi, and Frank van Berkum -- 16. Claims reserving and IBNR / Markus Gesmann.
|a"This book aims to provide a broad introduction to computational aspects of actuarial science, in the R environment. We assume that the reader is either learning, or is familiar with actuarial science. It can be seen as a companion to standard textbooks on actuarial science. This book is intended for various audiences: students, researchers, and actuaries. As explained in cite Kendrick et al. (2006) (discussing the importance of computational economics) \our thesis is that computational economics o ers a way to improve this situation and to bring new life into the teaching of economics in colleges and universities ... computational economics provides an opportunity for some students to move away from too much use of the lecture-exam paradigm and more use of a laboratorypaper paradigm in teaching under graduate economics. This opens the door for more creative activity on the part of the students by giving them models developed by previous generations and challenging them to modify those models." Based on the assumption that the same holds for computational actuarial science, we decided to publish this book. As claimed by computational scientists, computational actuarial science might simply refer to modern actuarial science methods. Computational methods started probably in the 1950s with Dwyer (1951) and von Neumann (1951). The rst one emphasized the importance of linear computations, and the second one the importance of massive computations, using random number generations (and Monte Carlo methods), while (at that time) access to digital computers was not widespread"--|cProvided by publisher.
內容簡介top Computational Actuarial Science With R 簡介 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/C++ embedded codes.After an introduction to the R language, the book is divided into four parts. The first one addresses methodology and statistical modeling issues. The second part discusses the computational facets of life insurance, including life contingencies calculations and prospective life tables. Focusing on finance from an actuarial perspective, the next part presents techniques for modeling stock prices, nonlinear time series, yield curves, interest rates, and portfolio optimization. The last part explains how to use R to deal with computational issues of nonlife insurance.Taking a do-it-yourself approach to understanding algorithms, this book demystifies the computational aspects of actuarial science. It shows that even complex computations can usually be done without too much trouble. Datasets used in the text are available in an R package (CASdatasets) from CRAN.