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.

  • Abstract

    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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      [2] MA Tabatabai, JJ Kengwoung Keuomo and K. P. Singh, “A new Robust method for nonlinear regressionâ€, Journal of biometrics and biostatistics, Vol. (5). (2014).

      [3] Xu Zheng, “Testing heteroscedasticity in nonlinear and nonparametric regressionâ€, Canadian Journal of statistics, Vol. (37), issue 2, (2009), Pp: 282-300.

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      [7] Vasilyev D.M, “Theoretical and Practical Aspects of linear and nonlinear models order reduction Techniquesâ€, MIT, USA, (2008).

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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

    Received date: 2018-10-08

    Accepted date: 2018-10-08

    Published date: 2018-10-02