This paper is devoted to studying the asymptotic behavior of LS-estimators in constrained nonlinear regression problems. Here the constraints are given by nonlinear equalities and inequalities. Thus this is a very general setting. Essentially this kind of estimation problem is a stochastic optimization problem. So we make use of methods in optimization to overcome the difficulty caused by nonlinearity in the regression model and given constraints.
@article{1032526971,
author = {Wang, Jinde},
title = {Asymptotics of least-squares estimators for constrained nonlinear regression},
journal = {Ann. Statist.},
volume = {24},
number = {6},
year = {1996},
pages = { 1316-1326},
language = {en},
url = {http://dml.mathdoc.fr/item/1032526971}
}
Wang, Jinde. Asymptotics of least-squares estimators for constrained nonlinear regression. Ann. Statist., Tome 24 (1996) no. 6, pp. 1316-1326. http://gdmltest.u-ga.fr/item/1032526971/