A Memoir on Model Selection Criterion between Two Nested and Non-Nested Stochastic Linear Regression Models

 
 
 
  • Abstract
  • Keywords
  • References
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  • Abstract


    The main purpose of this paper is to discuss some applications of internally studentized residuals 9n the model selection criterion between two nested and non-nested stochastic linear regression models. Joseph et.al [1] formulated various proposals from a Bayesian decision-theoretic perspective regarding model selection Criterion. Oliver Francois et.al [2] proposed novel approaches to model selection based on predictive distributions and approximations of the deviance. Jerzy szroeter [3] in his paper depicted the development of statistical methods to test non-nested models including regressions, simultaneous equations. In particular new criteria for a model selection between two nested/ non-nested stochastic linear regression models have been suggested here.

     

     


  • Keywords


    Test statistic, OLS residual sum of squares, nested and non-nested stochastic linear regression model, internally studentized residuals, OLS estimator.

  • References


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      [11] Taylor, H.M. and Samuel karlin, (1998), “An Introduction to Stochastic Modelling”, Academic Press, London, 978-0-12-684887-8.

      [12] Nafeez Umar, S. and Balasiddamuni, P. (2013), “Statistical Inference on Model Specification in Econometrics”, LAMBERT Academic Publishing, Germany.

      [13] Ramana Murthy .B. et.al (2011), “A modified criterion for model selection”, Proceedings of ICMS -2011, ISBN-978-81-8424-743-5.

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Article ID: 21219
 
DOI: 10.14419/ijet.v7i4.10.21219




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