Asymptotic Theory of Estimation in Nonlinear Regression

1987 ◽  
pp. 141-165 ◽  
Author(s):  
B. L. S. Prakasa Rao
Keyword(s):  
Nonlinear Regression ◽  
Asymptotic Theory

scholarly journals A Discourse on the Estimation of Nonlinear Regression Model

2018 ◽  
Vol 7 (4.10) ◽  
pp. 992
Author(s):  
B. Mahaboob ◽  
B. Venkateswarlu ◽  
J. Ravi Sankar ◽  
J. Peter Praveen ◽  
C. Narayana
Keyword(s):  
Regression Model ◽  
Nonlinear Regression ◽  
Asymptotic Theory ◽  
Pso Algorithm ◽  
Nonlinear Regression Model ◽  
Time Varying ◽  
Estimation Of Parameters ◽  
Research Article ◽  
Regression Functions ◽  
Heteroscedastic Errors

The present study evaluates an estimation for regression model which are nonlinear with Goldfeld, Quandt and exponential structure for heteroscedastic errors. An IENLGLS (Iterative Estimated Nonlinear Generalised Least Squares) estimator based on Goldfeld and Quandt for parametric vector has been derived in this research article. Volkan   Soner Ozsoy e.t.al [1], in their paper, proposed an effective approach based on the particle Swarm Optimisation (PSO) algorithm in order to enhance the accuracy in the estimation of parameters of nonlinear regression model. Ting Zhang et.al [2], in their article, established an asymptotic theory for estimates of the time-varying regression functions. Felix Chan et.al [3], in their paper, proposed some principals which are sufficient for asymptotic normality and consistency of the MLH estimator 

Asymptotic Theory of Nonlinear Regression

1999 ◽  
Vol 94 (446) ◽  
pp. 653
Author(s):  
Arnold Stromberg ◽  
Mai Zhou ◽  
Alexander V. Ivanov
Keyword(s):  
Nonlinear Regression ◽  
Asymptotic Theory

LEAST SQUARES ESTIMATION FOR NONLINEAR REGRESSION MODELS WITH HETEROSCEDASTICITY

2021 ◽  
pp. 1-23
Author(s):  
Qiying Wang
Keyword(s):  
Least Squares ◽  
Nonlinear Regression ◽  
Regression Models ◽  
Asymptotic Theory ◽  
Nonlinear Least Squares ◽  
Least Squares Estimation ◽  
Nonlinear Regression Models ◽  
Nonlinear Least Squares Estimation ◽  
New Framework

This paper develops an asymptotic theory of nonlinear least squares estimation by establishing a new framework that can be easily applied to various nonlinear regression models with heteroscedasticity. As an illustration, we explore an application of the framework to nonlinear regression models with nonstationarity and heteroscedasticity. In addition to these main results, this paper provides a maximum inequality for a class of martingales, which is of interest in its own right.

Nonlinear Regression

2011 ◽  
Author(s):  
Stephen J. Guastello
Keyword(s):  
Nonlinear Regression
Sign in / Sign up

Export Citation Format

Share Document