Hi all,
While working with signed-error distributions that are often skewed, I noticed
that using `mad(x)` with the default constant (1.4826) can unintentionally
widen
the dispersion estimate enough to mask important structure in the data.
For diagnostic and tolerance-testing workflows, especially when symmetry cannot
be assumed, using `mad(x, constant = 1)` gives a more interpretable “bulk”
interval because it reflects the raw median absolute deviation without scaling
to a normal model.
Before applying any parametric center/spread metrics, I’ve found it helpful to
plot a boxplot or ECDF to check for skew and tail weight. In skewed cases,
`constant = 1` behaves more robustly.
Is there any interest in discussing whether `constant = 1` might be a more
appropriate default for nonparametric workflows, or whether a note in the
documentation might help users choose the right constant for their context?
Thanks,
Thomas
[[alternative HTML version deleted]]
______________________________________________
[email protected] mailing list -- To UNSUBSCRIBE and more, see
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide https://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
