First, I think you need to keep explaining for the benefit of anyone
trying to pick up on this thread that LMS refers to a method devised by
[Timothy J.] Cole and others for handling growth curves. You earlier
gave a reference that was just Cole et al. 2008. Despite a strong hint
earlier from Stas Kolenikov, the further details of that reference are
still outstanding.
One of my dictionaries explains LMS as London Mathematical Society,
London Missionary Society, and London, Midland and Scottish Railway. It
is easy to guess that none of those apply but not so obvious that LMS
here does _not_ mean Least Median of Squares as devised by Rousseeuw, as
many statistically-minded people might imagine.
Rousseeuw, P.J. 1984. Least median of squares regression. Journal,
American Statistical Association 79: 871-880.
The more general point, which should be obvious except that many list
members act as if it were not true, is that the list includes people
from several quite different disciplines. Hence if you want to maximise
the readership of a question some explanations help a lot and rarely do
harm.
In terms of what you want to do:
Several people on this list should know much, much more about Cole's
method than I do but they are keeping quiet. I am surprised at the
implication that you need to feed skewness to Cole's method. That is
not, in particular, the case for -colelms- from SSC. I understood that
Cole's method was in essence designed to work well with the possibly
skew distributions that do occur and as such there is no specific need
to prepare the data or satisfy the assumptions of the method, as there
aren't any, except I guess that ages are accurate and size measurement
error negligible.
On the other hand, it may be that the missing reference, Cole et al.
2008, gives a quite different twist to the method, but then we are back
to my earlier point.
In general ignoring some fraction of data in the tail seems a very bad
idea unless it is obvious that the values concerned are all
untrustworthy. Even them some sensitivity analysis (with outliers vs
without outliers) would seem advisable.
Nick
[email protected]
Richard Palmer-Jones
Yes, I have been planning to use LMS method - basically adding the
adult parameters to the child hood ones given there. LMS needs
skewness - hence my interest. I am only interested in the adults older
that 25 (when both males and females have reached their full height)
so complicated smoothing is not necessary.
Yes, NHANES has heavy weighting which makes a considerable difference
to estimates (and false PSUs).
However, since the skewness reported by summarize is positive in
adults I am wondering whether a simpler procedure is to truncate the
parameter for valuies > 2.5sd, or to transform to logs, or some such
and work in them. Unfortunately ln(weight) is also skewed.
> Stas Kolenikov
>
> To Nick: yes, I've used skewness and kurtosis to test for normality a
> bunch of times (and there's a famous Mardia's multivariate
> generalization that I programmed up :)). But frankly I personally
> don't remember seeing confidence intervals on skewness anywhere at
> all. Estimation and testing are two related ways of looking at data
> with statistics, but with skewness and kurtosis you really estimate
> something to see that it is close enough to zero... and sometimes you
> don't even estimate a thing and go straight to the test statistic.
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