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.
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-- Bert Gunter Genentech Nonclinical Biostatistics Internal Contact Info: Phone: 467-7374 Website: http://pharmadevelopment.roche.com/index/pdb/pdb-functional-groups/pdb-biostatistics/pdb-ncb-home.htm
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