standard error for quantile

Petr,

You can  do:

require(quantreg)
summary(rq(x ~ 1, tau = c(.10,.50,.99))


url:    www.econ.uiuc.edu/~roger            Roger Koenker
email    rkoenker at uiuc.edu            Department of Economics
vox:     217-333-4558                University of Illinois
fax:       217-244-6678                Urbana, IL 61801

On Oct 30, 2012, at 9:37 AM, Bert Gunter wrote:

Petr:

1. Not an R question.

2. You want the distribution of order statistics. Search on that. It's
basically binomial/beta.

-- Bert

On Tue, Oct 30, 2012 at 6:46 AM, PIKAL Petr <petr.pikal at precheza.cz> wrote:

Dear all

I have a question about quantiles standard error, partly practical
partly theoretical. I know that

x<-rlnorm(100000, log(200), log(2))
quantile(x, c(.10,.5,.99))

computes quantiles but I would like to know if there is any function to
find standard error (or any dispersion measure) of these estimated
values.

And here is a theoretical one. I feel that when I compute median from
given set of values it will have lower standard error then 0.1 quantile
computed from the same set of values.

Is it true? If yes can you point me to some reasoning?

Thanks for all answers.
Regards
Petr

PS.
I found mcmcse package which shall compute the standard error but which
I could not make to work probably because I do not have recent R-devel
version installed

Error in eval(expr, envir, enclos) :
could not find function ".getNamespace"
Error : unable to load R code in package 'mcmcse'
Error: package/namespace load failed for 'mcmcse'

Maybe I will also something find in quantreg package, but I did not
went through it yet.

-- 

Bert Gunter
Genentech Nonclinical Biostatistics

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