Re: [R] fdHess function

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 wrote: > Hi Doug: I was just looking at this coincidentally. When X is a v

Re: [R] fdHess function

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, ev

Re: [R] fdHess function

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 matr