Another short answer:
http://www.stata.com/statalist/archive/2007-09/msg00147.html
and long answer:
Even the -svy- commands do not give you the test you often want, for
whether the means/medians/etc. are the same for two data generating
processes (say, men and women) or the means/medians/etc. of the
distributions in some superpopulation of possible populations are
equal. For an unstratified survey sample, you can usually just account
for clustering (on PSU) and weights and ignore the fpc and get the
right answer, but for stratified samples, a theoretically justifiable
test may not be programmed. However, using weights (either as
aweights or pweights) and bootstrapping with the cluster option will
often give accurate inference when your options are limited. There is
also a strata option for bootstrap, but weights+cluster may get you
the best performance--in the absence of a proof, you can always run
some simulations to convince yourself it will work for your particular
problem (by generating datasets that look more or less like yours).
On Sat, Mar 29, 2008 at 12:15 PM, Stas Kolenikov <[email protected]> wrote:
> Short answer: neither one fits well enough into the paradigm of survey
> sampling, so coming up with fully justifiable implementation is not
> straightforward.
>
> Long answer.
>
> For the first one, all rank tests implicitly assume the data are
> i.i.d., and I don't think very clear analogies are possible with
> survey data. There are no estimating equations to work with; you
> probably would be able to get the distribution of the test statistic
> over repeated sampling, but it won't be nearly as nice as the textbook
> distribution.
>
> For the second one, -qreg-is a heavily model-based concept: that for
> any combination of explanatory variables, there's a well defined
> distribution of responses over which the median can be computed. The
> straight design perspective, on the other hand, says that there are
> only so many individuals in the finite population, so there is no talk
> about conditional distributions. So one needs to invent some sort of a
> hybrid framework to incorporate both model and design ideas, and they
> don't always go hand in hand. A basic introduction to the subject is a
> chapter by Binder and Roberts in 2003 Analysis of Survey Data book
> (http://doi.wiley.com/10.1002/0470867205.ch3) -- I say introductory
> because they consider the simplest possible situations, but they still
> operate with big-O small-O in probability. Conceptually, it should
> still be possible to formulate median regression for sample surveys,
> as it is linked to a minimization problem, and thus can be cast in
> terms of estimating equations. Then you need to say, "If I had the
> full population, I would run this same median regression on it, and
> get some numbers from this census estimation procedure. Now, what I
> can hope for with the sample is that my estimates are going to be
> consistent for those numbers that came out of the census problem". I
> don't really know if that was done for quantile regression; for linear
> regression, the comparable result goes back to mid 1970s due to Wayne
> Fuller, and for generalized linear models, to David Binder's 1983
> paper. Median regression is somewhat trickier though, as the function
> being minimzed, the sum of absolute deviations, is not differentiable,
> so the standard tools like the delta method are not applicable.
>
>
>
> On 3/29/08, Mohammed El Faramawi <[email protected]> wrote:
> > Hi,
> > I am trying to run non-parametric tests using survey
> > data ( probability weighted). unfortunately I can not
> > find commands which takes into the consideration the
> > pweight. I am interested in qreg (median regression)
> > and Mann-Whitney test. Is there any way to do this by
> > Stata? Thank you
> > Mohammed Faramawi, MD,Phd,MPH,Msc
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