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

  • Authors

    • C. Narayana
    • B. Mahaboob
    • B. Venkateswarlu
    • J. Ravi sankar
    https://doi.org/10.14419/ijet.v7i4.10.21219

    Received date: October 7, 2018

    Accepted date: October 7, 2018

    Published date: October 2, 2018

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

  • 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.

  • References

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  • How to Cite

    Narayana, C., Mahaboob, B., Venkateswarlu, B., & Ravi sankar, J. (2018). A Memoir on Model Selection Criterion between Two Nested and Non-Nested Stochastic Linear Regression Models. International Journal of Engineering and Technology, 7(4.10), 529-531. https://doi.org/10.14419/ijet.v7i4.10.21219