- use LAR for data with less outliners/anomalies in your data. (each datapoint is as important as one other)
- use Bisquare for data which has outliners, but where you think that these outlines are due to i.e. disturbances and you don't want that your fit is affected by these outliners.
What is the difference between LAR, and the Bisquare Remain Robust in regression (Curve Fitting Tool)?
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Nuno Bouça on 17 Mar 2015
Commented: Lars Abrahamsson on 29 Jun 2021
What is the difference between LAR, Bisquare and other options Robust mode?
I'm creating regression models and with these commands I can many great results but need to know what I'm wearing.
I appreciate any help. Thank U.
Christiaan on 18 Mar 2015
Edited: Christiaan on 18 Mar 2015
Least absolute residuals (LAR) — The LAR method finds a curve that minimizes the absolute difference of the residuals, rather than the squared differences. Therefore, extreme values have a lesser influence on the fit.
Bisquare weights — This method minimizes a weighted sum of squares, where the weight given to each data point depends on how far the point is from the fitted line. Points near the line get full weight. Points farther from the line get reduced weight. Points that are farther from the line than would be expected by random chance get zero weight. For most cases, the bisquare weight method is preferred over LAR because it simultaneously seeks to find a curve that fits the bulk of the data using the usual least-squares approach, and it minimizes the effect of outliers.
This information can be found on this Mathworks website.
Also to make a summary:
Kind regards, Christiaan van Ommeren
Lars Abrahamsson on 29 Jun 2021
There is also the option
opt.Robust = "On"
what is the difference with this one compared to "LAR" and "Bisquare"?
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