A Discourse on the Estimation of Nonlinear Regression Model
B. Mahaboob, B. Venkateswarlu, J. Ravi Sankar, J. Peter Praveen, Chandan Alenahalli Narayana · International Journal of Engineering & Technology · 2018
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