From: Liaw, Andy <andy_liaw_at_merck.com>

Date: Thu 10 Mar 2005 - 00:55:01 EST

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Thu Mar 10 00:59:20 2005

Date: Thu 10 Mar 2005 - 00:55:01 EST

Where in the help file of sd() do you see the claim that it produces
unbiased estimate? Try the following:

sample.sizes <- 3:30

reps <- 5000

set.seed(1)

mean.vars <- sapply(sample.sizes,

function(n) mean(sd(matrix(rnorm(n*reps), nc=reps))^2))plot(sample.sizes,mean.vars)

I.e., the sample variance, or sd()^2, is unbiased for the true variance. If U is an unbiased estimator of a parameter theta, f(U) is _not_ necessarily unbiased for f(theta). It would be if f() is linear.

Andy

*> From: Roy Werkman
**>
**> Hi,
**>
*

> Can anyone help me with the following. I have been using R for Monte

*> Carlo simulations and got some results I couldn't explain. Therefor I
**> performed following short test:
**>
**> --------------
**> mean.sds <- NULL
**> sample.sizes <- 3:30
**>
**> for(N in sample.sizes){
**> dum <- NULL
**> for(I in 1:5000){
**> x <- rnorm(N,0,1)
**> dum <- c(dum,sd(x))
**> }
**> mean.sds<- c(mean.sds,mean(dum))
**> }
**> plot(sample.sizes,mean.sds)
**> --------------
**>
**> My question is why don't I get 1 as a result from my sd() for small
**> sample sizes? According to the help, sd() is unbiased, which anyway
**> would not explain the small offset... Is it something in rnorm()?
**>
**> Thanx,
**> Roy
**>
**>
**> --
**> The information contained in this communication and any\ >...{{dropped}}
*

R-help@stat.math.ethz.ch mailing list

https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide! http://www.R-project.org/posting-guide.html Received on Thu Mar 10 00:59:20 2005

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