Maximum pseudo likehood vs tweedie.
Le 5 mai 2010 ? 22:19, "RATIARISON Eric" <ERATIARISON at monceauassurances.co m> a ?crit :
Hello everybody, I?m interested in rating premium insurance, with the idea to impleme nt under R the theory linked in the paper attached (only hypothesis= distribution belongs to family exponential, No a priori specification) I?ve already have a gauss (software named?) code which do this with a package named CML (constrained maximum likehood).
Hello Eric To do constrained optimisation you can use the pkg Rsolnp, which I test since last week. It is very powerful.
With R, I?ve searched similar pack/functions like nlminb, maxLik and optim or other function in which I can precise the analytic functi on of loglik, gradient and hessian related to family exponential. But with no success (Newton raphson unavaible with fixes gr/hessian (own mistake ?) ,
Another solution is to use the genetic algorithm and to put the constraint as a penalty. rgenoud implements the GA. By the way the package fitdistrplus let you to use a custom optimisation function, and give you other statistics on a distribution fit.
often some problem of memory (matrix size : 550 000 rows x 14 covariables, and finally not sure that it concerned pseudo likehood.
If you have problem of memory, the only good solution is to have more RAM on your computer and/or to use a 64 bit OS, see R-help on this topic.
Could Tweedie cover this kind of preoccupation?
See the cran task view, I think there is a pkg implementing this distribution. Christophe
Anyone has try with R do to this ? Thanks for your help . E.ratiarison <robust_inference_in_ratingmodels.pdf>
_______________________________________________ R-SIG-insurance mailing list R-SIG-insurance at r-project.org https://stat.ethz.ch/mailman/listinfo/r-sig-insurance