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From: Matt J on 28 Jul 2010 18:26
"Samuel Edwards" <DJeter1234(a)AOL.com> wrote in message <i2q59p$ki5$1(a)fred.mathworks.com>...

> Sorry if that was unclear, the objective function m-file takes 1/variance as the input, but i define an anonymous function that takes s.d. as an input and feed this to the solver. So fmincon should be taking steps/gradients/hessians in terms of s.d. There may be a small amount of noise in the function because the integral/bounds are only an approximation, although if so it's not visible when i plot the area around a "minimum." However, I think it's probably poor scaling, but because I need this program to run automatically, I can't think of a better way to rescale than minimize sum(([1;1;1;1;1].*previous best guess-actual).^2). I need it to run automatically because the actual data has a somewhat low n, and I'm attempting to see what the optimization recovers from other data-sets that could have been generated by the "true" coefficients.
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My only other idea is that you create a simulated, but realistic, version of your parameter estimation problem where you know the ground truth parameter values. You could then do a numerical computation of the Hessian at the known global minimum (i.e. at the groud truth parameters). You could then use this Hessian as the basis for your parameter scaling.
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