[Date Prev][Date Next][Thread Prev][Thread Next][Date index][Thread index]
st: RE: RE: distribution of the kernel density
> > Someone knows if there is an statistic which I could use
> to asses if the
> > kernel density for a given variable follows a normal
> distribution ?
> How about:
> kdensity var1, gen(x y)
> qnorm y
> sktest y
The second variable which -kdensity- generates
is a density, which is not a suitable input
for -qnorm- or -sktest-.
Setting that aside, passing
data through a kernel density estimation
command can add no information suitable
for _any_ formal test of normality or
non-normality, and at best it obscures
the testing issue. To see this, note that
if you choose a Gaussian kernel and
increase its width, inevitably the
"estimated" distribution approaches Gaussian
form. More generally, any test would depend
on which kernel you choose
and on its width as much as on the
sample data, which makes no sense.
I support Lee's notion of using -qnorm-, but
on the original data. Another plot
which deserves a brief experiment
is -dpplot- on SSC.
For reasons often rehearsed on
this list, -swilk-, -sfrancia-
and -sktest- are in practice almost
always less illuminating than graphs.
* For searches and help try: