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 >
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