E.g. sample estimator bias for small n (which still is the case when the std is debiased
Show how the normal distribution pdf cleans up when L-scale is used to standardize.
Showing the effects of heavy tails and outliers is (also) low-hanging fruit.
Compare:
- Sample STD (debiasing it might be a nice flex)
- Mean absolute deviation (maybe)
- MAD (median absolute deviation)
- IQR (inter-quartile range)
- L-scale
- TL-scale
It might be worth mentioning that IQR is also an L-statistic like L-moments are (MAD isn't, but median is)
Distributions:
- uniform (i.e. the simplest polynomial, and the L-moment's "base distribution")
- normal (do the disappearance trick of
sqrt(pi))
- logistic (
l2==1 is a nice flex)
- t (df = 2 or 3)
- log-normal?
E.g. sample estimator bias for small
n(which still is the case when the std is debiasedShow how the normal distribution pdf cleans up when L-scale is used to standardize.
Showing the effects of heavy tails and outliers is (also) low-hanging fruit.
Compare:
It might be worth mentioning that IQR is also an L-statistic like L-moments are (MAD isn't, but median is)
Distributions:
sqrt(pi))l2==1is a nice flex)