Re: Curve fitting help needed
- From: "Phil Sherrod" <phil.sherrod@xxxxxxxxxxxxxxxxxxx>
- Date: Fri, 9 Dec 2005 16:50:01 GMT
On 9-Dec-2005, Nicros <da@xxxxxx> wrote:
> 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.
Huber's M-regression may be what you're looking for.
--
Phil Sherrod
(phil.sherrod 'at' sandh.com)
http://www.dtreg.com (decision tree and SVM predictive modeling)
http://www.nlreg.com (nonlinear regression)
.
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