The formula that you give --- which is exactly the same as that which appears in Cramer, page 369, would appear to imply that the variance is infinite when f(Q.p) = I have read at least 20 empirical papers where this is applied either in the time-series or the cross-sectional dimension and haven't seen a mention of standard error choice. The default value is “sqroot”, which uses the square root of the sample size. Only the first elements of the logical arguments are used.

I got values for tau but I did not manage to evaluate errors or variances of these values. Is there anything that one can do in instances where f(Q.p) = 0? For different distributions this can be reversed as Jim pointed out. I know thatx<-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 tofind standard error (or any dispersion measure) of these estimatedvalues.And here is a

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 Why don't cameras offer more than 3 colour channels? (Or do they?) Totally Invertible Submatrices Test a variable in a set entries tag What exactly does it mean for a scalar reply | permalink Ted Harding The general asymptotic result for the pth quantile (0 Date: 30-Oct-2012 Time: 17:40:55 This message was sent by XFMail Ted Harding at Oct 30, 2012 at I expect that if you looked at different sample sizes you'd find that variance eventually decreases slower than 1/n, perhaps n^(-2/3) or something For p=0.51, the asymptotics probably aren't going to

g a function such that the qth quantile of the univariate distribution function of g(x) is the quantity of interest. Which lane to enter on this roundabout? (UK) Would the phrase, "in my area," be a non-restrictive clause? Search on that. n number of observations.

Roger Koenker-3 Threaded Open this post in threaded view ♦ ♦ | Report Content as Inappropriate ♦ ♦ Re: standard error for quantile In reply to this post by PIKAL 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 Human vs apes: What advantages do humans have over apes? Johnson, N.

Jones, G. Thanks anyway. 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. >>> Please try the request again.

I also found some function for quantile se computing in Hmisc package. the inverse normal CDF values). ##### Checking for Normality with the Inter-Quartile Range and the Standard Deviation ##### By Eric Cai - The Chemical Statistician # Create a vector of standard The default option assumes that the errors are iid, while the option iid = FALSE implements the proposal of Koenker Machado (1999). and Wilks, A.

Try this: > > x<-sample(-5:5,1000,TRUE, > prob=c(0.2,0.1,0.05,0.04,0.03,0.02,0.03,0.04,0.05,0.1,0.2)) > x<-ifelse(x<0,x+runif(1000),x-runif(1000)) > hist(x) > mcse.q(x, 0.1) > $est > [1] -3.481419 > > $se > [1] 0.06887319 > > mcse.q(x, 0.5) > Rolf Turner-3 Threaded Open this post in threaded view ♦ ♦ | Report Content as Inappropriate ♦ ♦ Re: standard error for quantile In reply to this post by ted.harding-3 Search on that. Join them; it only takes a minute: Sign up Here's how it works: Anybody can ask a question Anybody can answer The best answers are voted up and rise to the

It's > basically binomial/beta. > > -- Bert > > On Tue, Oct 30, 2012 at 6:46 AM, PIKAL Petr <[hidden email]> wrote: >> Dear all >> >> I have a Search on that. S. For different distributions this > can be reversed as Jim pointed out. > > Did I manage to understand? > > Thank you very much. > Regards > Petr Yes, it

Regards Petr > > -- Bert > > On Tue, Oct 30, 2012 at 6:46 AM, PIKAL Petr <[hidden email]> > wrote: > > Dear all > > > > I q the quantile of interest. Bootstrap is preferable because it makes no assumption about the distribution of response (p. 47, Quantile regressions, Hao and Naiman, 2007). Not an R question. 2.

mcse(x, method = "obm") mcse.q(x, 0.1, method = "obm") mcse.q(x, 0.9, method = "obm") # Estimate E(x^2) with MCSE using spectral methods. I feel that when I compute median fromgiven set of values it will have lower standard error then 0.1 quantilecomputed from the same set of values.Is it true? PIKAL Petr Threaded Open this post in threaded view ♦ ♦ | Report Content as Inappropriate ♦ ♦ Re: standard error for quantile In reply to this post by Roger method the method used to compute the standard error.

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, Thomas Lumley-2 Threaded Open this post in threaded view ♦ ♦ | Report Content as Inappropriate ♦ ♦ Re: standard error for quantile On Tue, Nov 13, 2012 at 11:59 url: www.econ.uiuc.edu/~roger Roger Koenker email [hidden email] Department of Economics vox: 217-333-4558 If length(n) > 1, the length is taken to be the number required.

Generated Tue, 25 Oct 2016 00:18:33 GMT by s_wx1085 (squid/3.5.20) Not an R question. >> >> 2. S. In Brooks, S., Gelman, A., Jones, G.

I tested various functions which revealed that on my lognorm data there is no big difference in error of median or 10% quantile. The formula that you give --- which is exactly the same as that which appears in Cramer, page 369, would appear to imply that the variance is infinite when f(Q.p) = If yes can you point me to some reasoning?Thanks for all answers.RegardsPetr["PS" deleted]The general asymptotic result for the pth quantile (0

M. If yes can you point me to some reasoning?Thanks for all answers.RegardsPetrPS.I found mcmcse package which shall compute the standard error but whichI could not make to work probably because I S. I know thatx<-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 tofind standard error (or any dispersion measure) of these estimatedvalues.And here is a

Better still, it's a polynomial, so you could evaluate theintegral exactly.-thomas--Thomas LumleyProfessor of BiostatisticsUniversity of Auckland reply | permalink Related Discussions [R] the standard error of the quantile [R] Retrieve regression se the Monte Carlo standard error. A numeric value may be provided if “sqroot” is not satisfactory. It's basically binomial/beta. -- Bert On Tue, Oct 30, 2012 at 6:46 AM, PIKAL Petr <[hidden email]> wrote: > Dear all > > I have a question about quantiles standard error,

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