A Discourse on the Estimation of Nonlinear Regression Model

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    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

     


  • Keywords


    Nonlinear regression model, Heteroscedastic error, nonlinear internally studentized residuals, OLS (Ordinary Least Squares), Regressor matrix.

  • References


      [1] Volkan Soner Ozsoy, H. Hasan ORKCU, “Estimating the parameters of nonlinear regression models”, Gazi University Journal Science, 29, 1, (2016), Pp: 187-199

      [2] Ting Zhang, Wei Biao Wu, “Time-Varying nonlinear regression models: Non parametric estimation and model selection”, The Annals of statistics, Institute of Mathematical statistics, Vol. (43), (2015), Pp: 741-768.

      [3] Felix Chan, Marcelo C. Medeiros, “Structure and asymptotic theory for nonlinear models with GARCH errors”, Science Direct, Vol. (16), Issue 1, (2015), Pp: 1-21.

      [4] Gordon K. Smyth, “Nonlinear regression, Encyclopedia of Environmetrics Vol. (3), (2002). Pp: 1405-1411.

      [5] Gurleen K. Popli , “A note on the instrumental variable estimators in the nonlinear models”, Journal of Quantitative Economics Vol.16. no.2, (2000), Pp: 31-36.

      [6] Davidian M. and Giltinon D.M (2003), “Nonlinear models for repeated measurement Data: An overview and update”, Journal of Agricultural, Biological and Environmental statistics (JABES), Vol. (8), (2003), Pp: 387-419.

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

      [8] E. Grafarent and J. Awange, “Application of linear and nonlinear models”, Springer Geophysics, (2012).

      [9] Bates D.M and Walts D.G, “Nonlinear regression: Iterative Estimation and Linear Approximations in Nonlinear regression Analysis and its Applications”, John Wiley and sons Inc Hobeken, NJ, USA, (2008).


 

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Article ID: 26642
 
DOI: 10.14419/ijet.v7i4.10.26642




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