> -----Original Message-----
> From: r-help-bounces at stat.math.ethz.ch [SMTP:r-help-bounces at
stat.math.ethz.ch] On Behalf Of Mark Miller
> Sent: Thursday, November 17, 2005 10:16 AM
> To: r-help at stat.math.ethz.ch
> Subject: [R] Standard Error
>
> I have worked out that when I fit data I get an estimate and a standard
error,
> but all the definitions I can find describe the standard error of a sample
as
> the standard deviation over the square root of the sample size, so if I am
> fitting to a log-normal distribution, what is the standard error associated
> with the standard deviation and why is it different from the standard error
> of the mean.
------------
One thing is the standard error of the
estimate_of_a_mean_
from
a random_sample_from_a_population,
whose formula you mentioned.
Another thing, though related of course, is the standard error of a
parameter_estimate
from
a model.
The standard error of a parameter estimate from a model is a measure
of the precision with which the parameter was estimated. The standard
lognormal distribution is a model with two parameters (there is another
with three parameters): the mean and the standard deviation. When you
fit that model -the lognormal distribution- to a sample, you are estimating
these two parameters. If you maximise the likelihood for your data as a
function of the two parameters the estimation process, if successful, will
produce the two estimates and the corresponding standard errors of those
estimates (plus the estimated covariance between the estimates). Both
parameters, the lognormal mean and the lognormal standard deviation, are
unknown and are estimated so that each one has its corresponding measure
of precision.
You can think of the standard error of a parameter estimate from a model at
least in two ways.
(1) Because maximum likelihood estimates tend to distribute normally, then
the standard errors of parameter estimates are the standard deviation parameter
estimates in a normal distribution whose mean is estimated by the maximum
likelihood estimate itself. For example the output report from the ADMB
statistical
system simply put the header Standard Deviation in the column for standard
errors
of parameter estimates. Presumably this is because the ADMB's author
subscribe
to this interpretation.
(2) You can also think of standard error of parameter estimates as measuring the
curvature of the likelihood function about the maximum likelihood estimate.
In the pure-likelihood theory of inference this is the preferred interpretation.
So
A.W.F. Edwards (1972, Likelihood, Cambridge UP) has renamed the standard
errors calling them "the span".
I hope this makes sense to you.
Ruben