Can you both exclude outliers from a fit and use robust weighting for the remaining data?

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L'O.G. 2022-2-21

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评论： Image Analyst 2022-2-21

In using the fit function, is it possible to simultaneously exclude outliers and use one of the robust fitting options to weight the remaining data? Is this ever warranted? I guess my question is partly having to do with the implementation and partly about what is appropriate or not conceptually. I have data where where the initial part fits to one distribution that I want to exclude, whereas the second part fits to the distribution that I want to fit.

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Answer 1

Image Analyst 2022-2-21

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Why can't you just preprocess the data by removing outliers with rmoutliers() or other functions and then do the fitting? If there are only a few outliers, then they may not influence the fit that much. If there are lots of outliers, you can use something like RANSAC in the Computer Vision Toolbox.

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L'O.G. 2022-2-21

Thanks, I didn't know about that function. Is there any preferred reason to use that rather than both excluding outliers and using robust least squares simultaneously?

Image Analyst 2022-2-21

RANSAC is normally used when there is a clear curve but it is buried in the presence of LOTS of noise. If you just have a little noise (like a few percent of points are "bad") then you should use isoutlier() or rmoutlier() or filloutlier().

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Answer 2

Sulaymon Eshkabilov 2022-2-21

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In your exercise, if it is known which part to include in the fit simulation and which part to exclude, then you can use just appropriate indexes of your data for a fit model calculation.

If you want to remove just outliers from the data, then rmoutliers() can do the work easily.