Here is a simple example where the derivative is approximated with the "derivative" macro of Scilab.
S. 2014-02-20 9:43 GMT+01:00 Stéphane Mottelet <[email protected]>: > Hello, > > If you can make the hypothesis that your data is corrupted by gaussian > noise, then you can approximate the covariance matrix of your estimated > parameters. Let p be the vector of parameters and r(p) the residual vector > given by > > r(p)=sigma^(-1)*(y-Y(p)) > > where y is your measuement vector, Y(p) the "simulated" measurement, sigma > a diagonal matrix with the std error for each measurement. If we denote by > > drdp(p) the derivative (or jacobian matrix) of r with respect to p then > the covariance matrice C of parameters can be estimated by > > > C=F^(-1) > > where > > F=drdp(p)' * drdp(p) > > is the Fisher information matrix. The diagonal terms of V give you the > variance of the parameters. Of course, even in the gaussian case, this is a > crude approximation.... > > S. > > > 2014-02-19 17:10 GMT+01:00 Yohann <[email protected]>: > > yes I know Denis, It was just an example to illustrate my question. >> My real dataset and function to fit are completely different and more >> complex. >> Thank you >> >> >> >> -- >> View this message in context: >> http://mailinglists.scilab.org/evaluate-error-on-each-parameter-calculated-with-leastsq-tp4028696p4028748.html >> Sent from the Scilab users - Mailing Lists Archives mailing list archive >> at Nabble.com. >> _______________________________________________ >> users mailing list >> [email protected] >> http://lists.scilab.org/mailman/listinfo/users >> > >
premier_ordre_stats.sce
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