A couple of weeks ago Vinay suggested using the LOESS algorithm to create smooth curves (separately) through the SSD and FPC points. LOESS has been succeeded by LOWESS and, finally LOCFIT, which is the 800lb gorilla of local regression fitting.

The LOCFIT algorithm uses local regression (i.e. fits over samples of the data) to generate smooth curves. There is an enormous body of literature on this, much of it summarized in the book

Local Regression and Likelikhood, by C. Loader
ISBN 0-387-98775-4

which also serves as documentation for the LOCFIT software. The techniques seem well established and accepted by the statistical community.

LOCFIT looks to be a very elegant approach, but, unfortunately, I have still not been able to glean any information as to how one introduces experimental errors into the regressions. The voluminous research in this field certainly deals with experimental data, so I’m not quite sure what to make of this.

One way around this might be to take a Monte-Carlo approach: resample the data using the experimental errors, generate a new smoothing function, and generate a measure of the distribution of the fit functions.

For those interested, I have a copy of the above book on loan.
It’s fascinating reading.

More about the actual code is available at this web site:
http://locfit.herine.net/

In addition, Ping Zhao asks: (paraphrasing) if you combine two separate sets of observations with vastly different numbers of data points in each, how do you weight them during a combined loess/lowess/locfit fit?

Comments and suggestions from statisticians are much appreciated!