Dear All,
I would like to devalue condition number and multi-collinearity in nonlinear regression. The reason we consider condition number (or multi-collinearity) is that this may cause the following fitting (estimation) problems; 1. Fitting failure (fail to converge, fail to minimize) 2. Unrealistic point estimates 3. Too wide standard errors If you do not see the above problems (i.e., no estimation problem with modest standard error), you do not need to give attention to the condition number. I think I saw 10^(n – parameters) criterion in an old version of Gabrielsson’s book many years ago (but not in the latest version). Best regards, Kyun-Seop Bae On Tue, 29 Nov 2022 at 22:59, Ayyappa Chaturvedula <ayyapp...@gmail.com> wrote: > Dear all, > I am wondering if someone can provide references for the condition number > thresholds we are seeing (<1000) etc. Also, the other way I have seen when > I was in graduate school that condition number <10^n (n- number of > parameters) is OK. Personally, I am depending on correlation matrix rather > than condition number and have seen cases where condition number is large > (according to 1000 rule but less than 10^n rule) but correlation matrix is > fine. > > I want to provide these for my teaching purposes and any help is greatly > appreciated. > > Regards, > Ayyappa >
