The cort package is now on cran ! The cort package provides S4 classes and methods to fit several copula models:
The classic empirical checkerboard copula and the empirical checkerboard copula with known margins, see Cuberos, Masiello and Maume-Deschamps (2019) https://arxiv.
After working on a bootstrapping framework for the Mack model, with a one-year point of view and with several triangles to bootstrap jointly, i decided to put some of my code into a litle package, mbmcl.
My actuarial thesis got published online there
This work took me a little more than one year to do, an was dealing with non-life reserving in solvency 2 context for the french decenial insurance contracts.
Introduction Suppose you have a dataset, and you are narowing possible machine learning models to 2 or 3 models, but you still cant choose which you want : Will the benefit of understandability from my CART cost me too much compare to a random forest or some bootsting ?
Introduction The log-normal model Generating dummy dataset. Checking model hypohtesis. Log-normality of \(y_t\) Linearity between \(y_t\) and \(x_t\) Results from the model References Introduction Under Solvency 2 framework, insurance compagnies can calculate undertaking specific parameters to modify their application of the standard formula, as dictates Commission-Européenne (2014) .
Which actuary does not know about Mack’s model ? Due to Mack (1991), this model is fairly simple. Suppose you have a triangle.
Ok seeing the origin dates of claims, thoose data are old.