>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.
Good idea. At this point I do not need to be so precise as to work with a mixture distribution, but I will keep this in mind. >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. I am trying to model loss severity. One common simplified approach is to sample from e.g. a gamma or lognormal distribution to determine the dollar value of each loss. My problem with this approach is that I have the individual insured amounts, so a $100,000 loss which could result from sampling from a lognormal distribution does not seem reasonable if the insured amount is $25,000, to put an example. That is why I thought of a damage distribution instead. I am not sure what you mean by using a plot analysis or re-sampling the data. I posted back to Ben yesterday and the post was not accepted yet, so it probably does not show in the thread, but there I stated I was going to use a beta distribution, so my problem is solved by now. If you want, we may continue this conversation privately. Many thanks. 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 > -- > View this message in context: > http://www.nabble.com/Percent-damage-distribution-tp21170344p21170344.html > Sent from the R help mailing list archive at Nabble.com. > > ______________________________________________ > R-help@r-project.org mailing list > https://stat.ethz.ch/mailman/listinfo/r-help > PLEASE do read the posting guide > http://www.R-project.org/posting-guide.html > and provide commented, minimal, self-contained, reproducible code. > -- Brian D. Ripley, rip...@stats.ox.ac.uk Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/ University of Oxford, Tel: +44 1865 272861 (self) 1 South Parks Road, +44 1865 272866 (PA) Oxford OX1 3TG, UK Fax: +44 1865 272595 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code. ----- ~~~~~~~~~~~~~~~~~~~~~~~~~~ Diego Mazzeo Actuarial Science Student Facultad de Ciencias Económicas Universidad de Buenos Aires Buenos Aires, Argentina -- View this message in context: http://www.nabble.com/Percent-damage-distribution-tp21170344p21175296.html Sent from the R help mailing list archive at Nabble.com. ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.