Robust estimators of scale, such as the median absolute deviation (MAD) and so on, are less affected by outliers than something like the basic standard deviation/variance. Firstly, is there a specific criterion to identify robust estimators, like a hard cutoff, or is it vague?
Secondly, would something like sigma-clipping a data set before calculating the variance/standard deviation count as a robust estimator?
By sigma-clipping, I mean iteratively:
- estimating the standard deviation.
- masking out points that are more than N standard deviations away from the mean (let's say N=4).
- repeating 1 & 2 on the masked dataset several times.