Linearization conditions for regression models with unknown variance parameter.
type: Item
instance of: scholarly article
title: Linearization conditions for regression models with unknown variance parameter.
zbMATH Open document ID: 1067.62547
DOI: 10.1023/A:1022239613534
author: Anna Jenčová
published in: Applications of Mathematics
publication date: 2000-09-14T00:00:00Z
full work available at URL: https://eudml.org/doc/33053
review text: A detailed analysis of criteria for a linearization of regression models is given. The frequently occuring situation when a variance parameter is unknown is solved. Several inequalities useful for users are found out. A test of intrinsic nonlinearity more powerful than that one used until now is also shown. The developed theory is nicely demonstrated on the Michaelis-Menten model.
Mathematics Subject Classification ID: 62J02
Mathematics Subject Classification ID: 62J05
zbMATH DE Number: 1508454
zbMATH Keywords: nonlinear regression models
zbMATH Keywords: linearization domains
zbMATH Keywords: linearization conditions
MaRDI profile type: Publication
cites work: Q3903870
cites work: Q4775958
cites work: Q4882908
cites work: On a linearization of regression models
cites work: Q4244920
cites work: Q3141890
OpenAlex ID: W65013193
description: scientific article; zbMATH DE number 1508454