A Treatise on Testing General Linear Hypothesis in Stochastic Linear Regression Model

  • Authors

    • C. Narayana
    • B. Mahaboob
    • B. Venkateswarlu
    • J. Ravi sankar
    • P. Balasiddamuni
    https://doi.org/10.14419/ijet.v7i4.10.21223

    Received date: October 7, 2018

    Accepted date: October 7, 2018

    Published date: October 2, 2018

  • OLS estimator, OLS residuals, RLS estimator, linear hypothesis, RLS residual vector, General linear hypothesis, PRESS, GLS, BLUS, BAVS, RSURE, stochastic linear regression model, studentized residuals, RLS estimators.

  • Abstract

    The main objective of this research article is to propose test statistics for testing general linear hypothesis about parameters in stochastics linear regression model using studentized residuals, RLS estimates and unrestricted internally studentized residuals. In 1998, M. Celia Rodriguez -Campos et.al [1] introduced a new test statistics to test the hypothesis of a generalized linear model in a regression context with random design. Li Cai et.al [2] provide a new test statistic for testing linear hypothesis in an OLS regression model that not assume homoscedasticity. P. Balasiddamuni et.al [3] proposed some advanced tools for mathematical and stochastical modelling.

  • References

    1. M. Celia Rodrignez-Campos Wenceslao ‘Manteiga and Ricardo Cao, “Testing the hypothesis of a generalized linear regression mod-el using nonparametric regression estimation”, Journal of statistical planning and inference, (1998), Pp: 99-122.
    2. Li Cai, Andraw F. Hayes, “A new test of linear hypothesis in OLS regression under heteroscedasticity of unknown form”, Journal of Education and behavioral statistics”, Vol. (33), (2008), Pp: 21-40.
    3. Balasiddamuni, P. et.al., “Advanced Tools for Mathematical and Stochastic Modeling”, Proceedings of the International Conference on Stochastic Modeling and Simulation, Allied Publishers, (2011).
    4. Byron J.T. Morgan, “Applied stochastic Modelling”, CRC Press, (2008), 978-1-58488-666-2.
    5. Berry L. Nelson, (1995), “Stochastic Modeling, Analysis and Simu-lation”, McGraw-Hill, (1995), 978-0070462137.
    6. Nelson, B.L. “Stochastic Modelling”, McGraw-Hill, New York, (1995), 0-486-47770-3.
    7. Taylor, H.M. and Samuel karlin, “An Introduction to Stochastic Modeling”, Academic Press, London, (1998), 978-0-12-684887-87
    8. Nafeez Umar, S. and Balasiddamuni, P, “Statistical Inference on Model Specification in Econometrics”, LAMBERT Academic Pub-lishing, Germany, (2013).

  • Downloads

  • How to Cite

    Narayana, C., Mahaboob, B., Venkateswarlu, B., Ravi sankar, J., & Balasiddamuni, P. (2018). A Treatise on Testing General Linear Hypothesis in Stochastic Linear Regression Model. International Journal of Engineering and Technology, 7(4.10), 539-542. https://doi.org/10.14419/ijet.v7i4.10.21223