Your question is better addressed to the R-help@R-project.org mailing list,
which I am copying on this reply.
You are confusing a statistical concept, the Fisher Information matrix,
with a numerical concept, the Hessian matrix of a scalar function of a
vector argument.
The Fisher information
Hi Doug: I was just looking at this coincidentally. When X is a vector, the
Fisher Information I_{theta} = the negative expectation of the second
derivatives of the log likelihood. So it's a matrix. In other words,
I_theta = E(partial^2 /partial theta^2(log(X,theta).) where X is a vector.
But,
I neglected to mention that, once you get either I_theta or some empirical
estimate
of it, you then invert it to get an estimate of the asymptotic covariance
matrix of the
MLE.
On Tue, Jan 22, 2013 at 3:48 PM, Mark Leeds marklee...@gmail.com wrote:
Hi Doug: I was just looking at this
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