Calculation of the Standard Error of Parameters in non-linear Regression
Today I recreated an estimation of standard error for a parameter analytically. I compared it with the standard Error traced by Mathematica and it is for two examples quite different.
The two examples were:
Because of this I recalculated in a program which does the estimation numerically (gnuplot). The traced value is very similar to the one by Mathematica. So my question: Does it Mathematica just numerically?
I used the analytical procedure usual in Newton-Gauss procedure:
Where H is the Hess matrix
I know this method is just a estimation, but a analytical method should be general better than anything numerical, am I right?
Re: Calculation of the Standard Error of Parameters in non-linear Regression
No, depends on the approximation in the analytic method, there will always be cases where a particular approximation falls down. The numerical method will be something like the Jackknife (or if the problem is suitable the Bootstrap) and should be fairly robust.
Originally Posted by Boojakascha