On information matrices in nonlinear experimental design
In the Gaussian nonlinear regression model we propose matrices which can be interpreted as measuring the information about the parameters in the case when they are estimated by least squares. The whole argument is based on a nonasymptotical approximative probability density of the least squares estimates. Proposals for nonlinear experimental design are presented.
nonasymptotical approximative probability density of the least squares estimates
scientific article; zbMATH DE number 4092592
scientific article