Not an R question as yet .....

In my limited experience (we have some insurance projets), 100% can occur, but otherwise a beta distbribution may suit, which suggests a mixture distribution. But start with an empirical examination (histogram, ecdf, density plot) of the distribution, since it may reveal other features.

The next question is 'why model'? For such a simple problem (a univariate distribution) a plot may be a sufficent analysis, and for e.g. simulation you could just re-sample the data.

On Thu, 25 Dec 2008, diegol wrote:


R version: 2.7.0
Running on: WinXP

I am trying to model damage from fire losses (given that the loss occurred).
Since I have the individual insured amounts, rather than sampling dollar
damage from a continuous distribution ranging from 0 to infinity, I want to
sample from a percent damage distribution from 0-100%. One obvious solution
is to use runif(n, min=0, max=1), but this does not seem to be a good idea,
since I would not expect damage to be uniform.

I have not seen such a distribution in actuarial applications, and rather
than inventing one from scratch I thought I'd ask you if you know one, maybe
from other disciplines, readily available in R.

Thank you in advance.

-----
~~~~~~~~~~~~~~~~~~~~~~~~~~
Diego Mazzeo
Actuarial Science Student
Facultad de Ciencias Económicas
Universidad de Buenos Aires
Buenos Aires, Argentina
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