[R-sig-ins] Maximum pseudo likehood vs tweedie.

[RATIARISON Eric]

Hello everybody,

I'm interested in rating premium insurance, with the idea to implement
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).

 

With R, I've searched similar pack/functions like nlminb, maxLik and
optim or other function  in which I can precise the analytic function of
loglik, gradient and hessian related to family exponential.

But with  no success (Newton raphson unavaible with fixes gr/hessian
(own mistake ?) , often some problem of memory (matrix size  : 550 000
rows x 14 covariables, and finally not sure that it concerned pseudo
likehood.

 

Could Tweedie  cover this kind of preoccupation?

 

Anyone has try with  R do to this ?

 

Thanks for your help .

 

E.ratiarison

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