On Thu, Feb 19, 2004 at 09:22:09AM -0800, Thomas Lumley wrote:
So, what is the _right_ way for obtatining SE? Why two those formulas above
differ?
If you are maximising a likelihood then the covariance matrix of the
estimates is (asymptotically) the inverse of the negative of the Hessian.
Dear R experts,
In R-intro, under the 'Nonlinear least squares and maximum likelihood
models' there are ttwo examples considered how to use 'nlm' function.
In 'Least squares' the Standard Errors obtained as follows:
After the fitting, out$minimum is the SSE, and out$estimates are the
On Thu, 19 Feb 2004, Timur Elzhov wrote:
So, what is the _right_ way for obtatining SE? Why two those formulas above
differ?
If you are maximising a likelihood then the covariance matrix of the
estimates is (asymptotically) the inverse of the negative of the Hessian.
The standard errors are
Minor correction: Most likely, Prof. Lumley's statement is
correct. However, as I'm sure he knows, it depends on what you are
maximizing or minimizing: If you are maximizing the log(likelihood),
then the NEGATIVE of the hessian is the observed information. This
latter should be
