*that*came from) should flow through to them. I've added them to my (tiny) blogroll at the side of the blog, but if you just cannot wait to see what they are all about, and I recommend it, you can just go here.

## Wednesday, May 18, 2011

### R-bloggers

As I decided to try and blog a little more often now, and touch on "R" every now and then, I decided to take R-bloggers up on their standing offer to include R-related feeds at their site. So, everything I tag with "rstats" (you can guess where

## Sunday, May 15, 2011

### Why method of moments doesn't always work

A number of years ago, someone asked me "why does my company need actuaries to fit curves, once I have the mean and standard deviation of my losses, isn't that enough?" I explained to him that not every distribution is completely determined by its mean and standard deviation (as the normal and lognormal are), and as at that point, I did not have "R" installed on my laptop, I demonstrated it to him in Excel. Having wanted to start blogging about "R", even ever so infrequently, I figured I'd toss together a little code to demonstrate.

The example I gave was to compare a gamma and a pareto distribution, each of which has mean 10,000 and a CV of 150% (making the standard deviation 15,000). I will spare all of you the algebra, but suffice to say, that using the Klugman-Panjer-Wilmot parameterization (which is used by most casualty actuaries in the past 20 years or so) the parameters of the gamma would be theta (R's scale) = 22500 and alpha (R's shape) = 4/9. The equivalent pareto would have theta (R's scale) = 26000 and alpha (R's shape) = 3.6.

Graphing the two (and Hadley, please forgive me for using default R' plotting, I left my ggplot book in the office;

To make things easier for me, I used the actuar package to do the graphing:

Obviously, the tails of the distributions, and thus the survival function at a given loss size, is different for the two, notwithstanding their sharing identical first two moments. So, this was just a brief but effective visualization as to how the first two moments do not contain all the information needed to find a "best fit," and why we like to use distributional fitting methods (maximum likelihood, maximum spacing, various minimum distance metrics like Cramer-von Mises, etc.) to get a better understanding of the potential underlying loss processes.

The example I gave was to compare a gamma and a pareto distribution, each of which has mean 10,000 and a CV of 150% (making the standard deviation 15,000). I will spare all of you the algebra, but suffice to say, that using the Klugman-Panjer-Wilmot parameterization (which is used by most casualty actuaries in the past 20 years or so) the parameters of the gamma would be theta (R's scale) = 22500 and alpha (R's shape) = 4/9. The equivalent pareto would have theta (R's scale) = 26000 and alpha (R's shape) = 3.6.

Graphing the two (and Hadley, please forgive me for using default R' plotting, I left my ggplot book in the office;

*mea culpa*) you can easily see how the distributions are rather different.To make things easier for me, I used the actuar package to do the graphing:

Created by Pretty R at inside-R.org

Obviously, the tails of the distributions, and thus the survival function at a given loss size, is different for the two, notwithstanding their sharing identical first two moments. So, this was just a brief but effective visualization as to how the first two moments do not contain all the information needed to find a "best fit," and why we like to use distributional fitting methods (maximum likelihood, maximum spacing, various minimum distance metrics like Cramer-von Mises, etc.) to get a better understanding of the potential underlying loss processes.

## Friday, May 13, 2011

### New look!

I decided to change things around a bit, and applied a new template and color scheme. Maybe now my next post won't take another five months! Let me know if you like the new look.

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