![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/902550/image.png)
Can you both exclude outliers from a fit and use robust weighting for the remaining data?
3 visualizzazioni (ultimi 30 giorni)
Mostra commenti meno recenti
L'O.G.
il 21 Feb 2022
Commentato: Image Analyst
il 21 Feb 2022
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.
0 Commenti
Risposta accettata
Image Analyst
il 21 Feb 2022
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.
![](https://www.mathworks.com/matlabcentral/answers/uploaded_files/902550/image.png)
2 Commenti
Image Analyst
il 21 Feb 2022
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().
Più risposte (1)
Sulaymon Eshkabilov
il 21 Feb 2022
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.
0 Commenti
Vedere anche
Categorie
Scopri di più su Descriptive Statistics in Help Center e File Exchange
Community Treasure Hunt
Find the treasures in MATLAB Central and discover how the community can help you!
Start Hunting!