Open AccessA Monograph on Nonlinear Regression Models
Author(s) -
B. Mahaboob,
B. Venkateswarlu,
C. Narayana,
J. Ravi sankar,
P Balasiddamuni
Publication year - 2018
Publication title -
international journal of engineering and technology
Language(s) - English
Resource type - Journals
ISSN - 2227-524X
DOI - 10.14419/ijet.v7i4.10.21277
Subject(s) - heteroscedasticity , nonlinear regression , mathematics , non linear least squares , nonlinear system , robust regression , ordinary least squares , generalized least squares , outlier , parametric statistics , nonparametric regression , statistics , least squares function approximation , regression analysis , estimator , econometrics , physics , quantum mechanics
This research article uses Matrix Calculus techniques to study least squares application of nonlinear regression model, sampling distributions of nonlinear least squares estimators of regression parametric vector and error variance and testing of general nonlinear hypothesis on parameters of nonlinear regression model. Arthipova Irina et.al [1], in this paper, discussed some examples of different nonlinear models and the application of OLS (Ordinary Least Squares). MA Tabati et.al (2), proposed a robust alternative technique to OLS nonlinear regression method which provide accurate parameter estimates when outliers and/or influential observations are present. Xu Zheng et.al [3] presented new parametric tests for heteroscedasticity in nonlinear and nonparametric models.