Contrary to what I understood at the first reading of Antoine's respons and what can do Bootstrapping, it seems that this method is well suited to do the job.
http://en.wikipedia.org/wiki/Bootstrapping_%28statistics%29#Resampling_residuals If I have well understood this link, after applying this algorithm you obtain a large number of occurrences (Nboot) of each one of your parameter (C1 C2 and C3 in my case) and then you can determine a value and a confidence interval: C1 = mean(C1boot)+/- std(C1boot)*1.95996 C2 = mean(C2boot)+/- std(C2boot)*1.95996 C3 = mean(C3boot)+/- std(C3boot)*1.95996 I think 1.95996 is for a confidence interval of 97.5% (from Student law), but not sure... If it is good, it looks easy to implement and understand. -- View this message in context: http://mailinglists.scilab.org/evaluate-error-on-each-parameter-calculated-with-leastsq-tp4028696p4028795.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
