Computational actuarial science with R
著者
書誌事項
Computational actuarial science with R
（The R series）（A Chapman & Hall book）
CRC, 2016
- : pbk
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注記
Originally published: 2015
Includes bibliographical references (p. 583-604) and index
内容説明・目次
内容説明
A Hands-On Approach to Understanding and Using Actuarial Models
Computational 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).
目次
Introduction. METHODOLOGY: Standard Statistical Inference. Bayesian Philosophy. Statistical Learning. Spatial Analysis. Reinsurance and Extremal Events. LIFE INSURANCE: Life Contingencies. Prospective Life Tables. Prospective Mortality Tables and Portfolio Experience. Survival Analysis. FINANCE: Stock Prices and Time Series. Yield Curves and Interest Rates Models. Portfolio Allocation. NON-LIFE INSURANCE: General Insurance Pricing. Longitudinal Models and Experience Rating. Claims Reserving and IBNR. Bibliography. Index. R Command Index.
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