Haines, Linda M. A note on the differential geometry of least squares estimation for nonlinear regression models. (English) Zbl 0830.62063 S. Afr. Stat. J. 28, No. 2, 73-91 (1994). Summary: The sum of squares function of a nonlinear regression model is itself nonlinear in the unknown parameters and may therefore have more than one stationary point with respect to those parameters. This situation leads to complications of a computational and an inferential nature and is therefore of some interest.Here, the relevant theory for characterizing observations on a nonlinear regression model for which the sum of squares functions has more than one stationary point is developed within a differential-geometric framework. MSC: 62J02 General nonlinear regression 53A99 Classical differential geometry Keywords:least squares; multiple stationary points; Michaelis-Menten model; sum of squares function PDFBibTeX XMLCite \textit{L. M. Haines}, S. Afr. Stat. J. 28, No. 2, 73--91 (1994; Zbl 0830.62063)