A Monograph on Nonlinear Regression Models

  • Authors

    • B. Mahaboob
    • B. Venkateswarlu
    • C. Narayana
    • J. Ravi sankar
    • P. Balasiddamuni
    2018-10-02
    https://doi.org/10.14419/ijet.v7i4.10.21277
  • Nonlinear regression model, Residual form of Squares, error variance, Least Squares Estimator, Parametric vector, variance-Covariance matrix, OLS.
  • 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.

     

     

  • References

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  • How to Cite

    Mahaboob, B., Venkateswarlu, B., Narayana, C., Ravi sankar, J., & Balasiddamuni, P. (2018). A Monograph on Nonlinear Regression Models. International Journal of Engineering & Technology, 7(4.10), 543-546. https://doi.org/10.14419/ijet.v7i4.10.21277