The Annals of Statistics

Asymptotic Theory of Nonlinear Least Squares Estimation

Chien-Fu Wu

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Abstract

For a linear regression model, the necessary and sufficient condition for the asymptotic consistency of the least squares estimator is known. An analogous condition for the nonlinear model is considered in this paper. The condition is proved to be necessary for the existence of any weakly consistent estimator, including the least squares estimator. It is also sufficient for the strong consistency of the nonlinear least squares estimator if the parameter space is finite. For an arbitrary compact parameter space, its sufficiency for strong consistency is proved under additional conditions in a sense weaker than previously assumed. The proof involves a novel use of the strong law of large numbers in $C(S)$. Asymptotic normality is also established.

Article information

Source
Ann. Statist., Volume 9, Number 3 (1981), 501-513.

Dates
First available in Project Euclid: 12 April 2007

Permanent link to this document
https://projecteuclid.org/euclid.aos/1176345455

Digital Object Identifier
doi:10.1214/aos/1176345455

Mathematical Reviews number (MathSciNet)
MR615427

Zentralblatt MATH identifier
0475.62050

JSTOR
links.jstor.org

Subjects
Primary: 62J02: General nonlinear regression
Secondary: 62F12: Asymptotic properties of estimators

Keywords
Nonlinear least squares estimator nonlinear model weak and strong consistency asymptotic normality strong law of large numbers in $C(S)$

Citation

Wu, Chien-Fu. Asymptotic Theory of Nonlinear Least Squares Estimation. Ann. Statist. 9 (1981), no. 3, 501--513. doi:10.1214/aos/1176345455. https://projecteuclid.org/euclid.aos/1176345455


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