Computational Actuarial Science with R by Arthur Charpentier

By Arthur Charpentier

A Hands-On method of knowing and utilizing Actuarial Models

Computational Actuarial technology with R presents an creation to the computational facets of actuarial technological know-how. utilizing easy R code, the e-book is helping the algorithms excited about actuarial computations. It additionally covers extra complicated subject matters, comparable to parallel computing and C/C++ embedded codes.

After an advent to the R language, the publication is split into 4 elements. the 1st one addresses technique and statistical modeling matters. the second one half discusses the computational features of existence coverage, together with lifestyles contingencies calculations and potential existence tables. targeting finance from an actuarial point of view, the following half offers thoughts for modeling inventory costs, nonlinear time sequence, yield curves, rates of interest, and portfolio optimization. The final half explains tips to use R to accommodate computational problems with nonlife insurance.

Taking a home made method of figuring out algorithms, this booklet demystifies the computational features of actuarial technology. It exhibits that even complicated computations can often be kept away from an excessive amount of hassle. Datasets utilized in the textual content are available an R package deal (CASdatasets) from CRAN.

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Equal((3/10-1/10),(7/10-5/10)) [1] TRUE √ Similarly, ( 2)2 is slightly different from 2, > sqrt(2)^2 == 2 [1] FALSE To go further on floating-point numbers (that can be represented exactly by a computer), see Goldberg (1991). 220446e-16 > 1+eps==1 [1] FALSE It does not mean that R cannot deal with smaller numbers, only that == cannot be used. 225074e-308 As mentioned earlier, the important idea with vectors is that they are (ordered) collections of elements of the same type, which can be numeric (in R), complex (in C), integer (in N), character for characters or strings, logical, that is FALSE or TRUE (or in {0, 1}).

6 Symbolic Expressions in R . . . . . . . . . . . . . . . . . . . . . . Basics of the R Language . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 Core Functions . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 From Control Flow to “Personal” Functions . . . . . . . . . . . . . 1 Control Flow: Looping, Repeating and Conditioning . . . 2 Writing Personal Functions . . . . . . .

Exercises . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 R for Actuarial Science? 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.

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