The Annals of Statistics

Asymptotics of least-squares estimators for constrained nonlinear regression

Jinde Wang

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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 information

Ann. Statist., Volume 24, Number 3 (1996), 1316-1326.

First available in Project Euclid: 20 September 2002

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Mathematical Reviews number (MathSciNet)

Zentralblatt MATH identifier

Primary: 62J02: General nonlinear regression
Secondary: 62J12: Generalized linear models

Nonlinear regression nonlinear constraints LS-estimator asymptotics stochastic optimization


Wang, Jinde. Asymptotics of least-squares estimators for constrained nonlinear regression. Ann. Statist. 24 (1996), no. 3, 1316--1326. doi:10.1214/aos/1032526971.

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