Asymptotics of least-squares estimators for constrained nonlinear regression

@inproceedings{Wang1996AsymptoticsOL,
  title={Asymptotics of least-squares estimators for constrained nonlinear regression},
  author={Jinde Wang},
  year={1996}
}
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. 

Citations

Publications citing this paper.
SHOWING 1-10 OF 17 CITATIONS

Asymptotics of estimates in constrained nonlinear regression with long-range dependent innovations

VIEW 6 EXCERPTS
CITES METHODS & BACKGROUND
HIGHLY INFLUENCED

Estimating GVAR weight matrices

VIEW 1 EXCERPT
CITES BACKGROUND

Fusion of hard and soft information in nonparametric density estimation

VIEW 1 EXCERPT
CITES BACKGROUND