On information matrices in nonlinear experimental design
type: Item
instance of: scholarly article
title: On information matrices in nonlinear experimental design
zbMATH Open document ID: 0667.62048
DOI: 10.1016/0378-3758(89)90009-8
author: Andrej Pázman
published in: Journal of Statistical Planning and Inference
publication date: 1989-01-01T00:00:00Z
review text: 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.
Mathematics Subject Classification ID: 62J02
Mathematics Subject Classification ID: 62K05
Mathematics Subject Classification ID: 62F10
zbMATH DE Number: 4092592
zbMATH Keywords: nonlinear least squares
zbMATH Keywords: Gaussian nonlinear regression model
zbMATH Keywords: nonasymptotical approximative probability density of the least squares estimates
zbMATH Keywords: nonlinear experimental design
MaRDI profile type: Publication
full work available at URL: 0378-3758(89)90009-8
OpenAlex ID: W2090837293
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description: scientific article; zbMATH DE number 4092592