Asymptotic theory of least squares estimator in a nonregular nonlinear regression model
type: Item
instance of: scholarly article
title: Asymptotic theory of least squares estimator in a nonregular nonlinear regression model
zbMATH Open document ID: 0558.62054
DOI: 10.1016/0167-7152(85)90004-5
published in: Statistics \& Probability Letters
publication date: 1985-01-01T00:00:00Z
review text: The asymptotic properties of the least squares estimator are derived for a nonregular nonlinear model via the study of weak convergence of the least squares process. This approach was adapted earlier by the author in the smooth case. The model discussed here is not amenable to analysis via the normal equations and Taylor expansions used by earlier authors.
Mathematics Subject Classification ID: 62J02
Mathematics Subject Classification ID: 62F12
Mathematics Subject Classification ID: 60F05
zbMATH DE Number: 3890546
zbMATH Keywords: least squares estimator
zbMATH Keywords: nonregular nonlinear model
zbMATH Keywords: weak convergence of the least squares process
author: B. L. S. Prakasa Rao
MaRDI profile type: Publication
full work available at URL: 0167-7152(85)90004-5
OpenAlex ID: W2055702249
cites work: Central Limit Theorems for Families of Sequences of Random Variables
cites work: Estimation of the Location of the Cusp of a Continuous Density
cites work: On Evaluating a Certain Integral
cites work: The rate of convergence of the least squares estimator in a non-linear regression model with dependent errors
cites work: On the exponential rate of convergence of the least squares estimator in the nonlinear regression model with Gaussian errors
description: scientific article; zbMATH DE number 3890546