The Annals of Statistics

Asymptotics of least-squares estimators for constrained nonlinear regression

Jinde Wang

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Abstract

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

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

Dates
First available in Project Euclid: 20 September 2002

Permanent link to this document
https://projecteuclid.org/euclid.aos/1032526971

Digital Object Identifier
doi:10.1214/aos/1032526971

Mathematical Reviews number (MathSciNet)
MR1401852

Zentralblatt MATH identifier
0862.62057

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

Keywords
Nonlinear regression nonlinear constraints LS-estimator asymptotics stochastic optimization

Citation

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


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References

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