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Re: st: clustering in quantile regressions with sampling weights


From   Nikhil Srivastava <nikhil.del85@gmail.com>
To   statalist@hsphsun2.harvard.edu
Subject   Re: st: clustering in quantile regressions with sampling weights
Date   Sun, 24 Oct 2010 19:00:39 -0700

Thanks everybody for your help. As I do not have the variables that
were used to construct the sampling weights I ca not use them as
predictors in my model. The only reason I was worried about using
bootstrapping to get standard errors for my quantile regressions is
that the Stata Manual warns against using bootstarp with weighted
calculations-"bootstrap is not meant to be used with weighted
calculations"- Page-209, Volume [R], Stata Manual. I was a bit
apprehensive if this warning applies in my case too.Thanks

Best,
Nikhil

On Fri, Oct 22, 2010 at 9:31 AM, Stas Kolenikov <skolenik@gmail.com> wrote:
> Michael refers to the likelihood-based approach to survey data
> analysis, in which conditioning on the design variables makes the
> probability weights superfluous (note that conditioning is a very
> vague term; adding the design variables to the regression may not
> suffice, as you'd have to add their powers and interactions for an
> approximation to effective conditioning). Unfortunately, I don't think
> this will work out with quantile regression, as this is not a
> likelihood-based method. In my experience, the bootstrap methods seem
> to work well, although formal proofs are convoluted and rely on rather
> complicated conditions on the designs and sampling probabilities, and
> small sample performance is basically unknown (Austin and I are
> running some simulations to try to identify the limits of
> applicability, but we don't have any particularly strong findings yet
> to report). See my description of the survey bootstrap in SJ 10 (3)
> and accompanying package.
>
> On Fri, Oct 22, 2010 at 12:44 AM, Michael N. Mitchell wrote:
>>  I know this is a longshot, but do you know and have the variables involved
>> in creating the sampling weights (e.g., gender, age, race, etc...). If you
>> have the variables that were used to create the sampling weights, then you
>> could include those variables as predictors in your model to account for
>> them.
>
> --
> Stas Kolenikov, also found at http://stas.kolenikov.name
> Small print: I use this email account for mailing lists only.
>
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