Curve fitting help needed



Hi,

I have a series of data points that I am fitting to a reference set. I am currently using a chebyshev polynomial approximation to minimize the error in the fit.

However, outliers and erronious data points have been causing a huge headache.

If there is one poor fitting data point, then all the good points that would fit perfectly (if this poor data point was removed) are fitted poorly. Removing the data points before fitting is not desireable, since I have no way of really knowing ahead of time which data points might be bad.

Is there a minimization fitting routine or algorithm out there that will minimize the maximum number of datapoints to the highest degree possible, while allowing poor fit of outliers? So that if I have 10 data points (for example) and one of them is significantly off, the algorithm allows this one point to shift as far as necessary to fit the other 9 perfectly? How about 5 and 5? No clue what would even need to happen here.

I am not a mathmatician, Im a software engineer and I didnt find anything in numerical recipes in c++ that sounded like it could do this.

Does such an algorithm even exist? Any suggestions or links are appreciated!

Thanks,
N
.



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