Some Improved Mixed Regression Estimators and their Comparison when Disturbance Terms follow Multivariate T-Distribution

Manoj Kumar, Vikas Bist, Man Inder Kumar

Abstract


The Mean square error matrices, bias vector and risk functions of proposed improved mixed regression estimators are obtained by employing the small disturbance approximation technique under the condition, when disturbance terms follows multivariate t-distribution. Further, the risk function criterion is used to examine the efficiency of proposed improved mixed regression estimators.

Keywords


Stochastic restrictions; Mixed regression estimator; Stein- rule estimator; Multivariate t-distribution

Full Text:

PDF

References


Chaturvedi, A. and Shukla, G., Stein-rule estimation in linear models with non-scalar error covariance matrix, Sankhya, Series B, 52, (1990), 293-304.

Chaturvedi, A. ,Wan, A. T. K. and Singh, S. P., Stein-rule restricted regression estimator in a linear regression model with non-spherical disturbances, Communications in Statistics, Theory and Methods, 30, (2001), 55-68.

Giles, A.J., Pretesting for linear restriction in a regression model with spherically symmetric distributions, Journal of Econometrics, 50, (1991), 377-398.

Judge, G. G. and Bock, M. E., The Statistical Implications of Pre-Test and Stein-Rule Estimators in Econometrics, North Holland, Amsterdam, 1978.

Kadane, J.B.,Comparison of k-class Estimators When disturbance are small, Econometrica, 39, (1971), 723-737.

Ohtani, K. and Wan, A. T. K., On the sampling performance of an improved Stein inequality restricted estimator, Australian and New Zealand Journal of Statistics, 40, (1998), 181-187.

Rao, C. R., Linear Statistical Inference and Its Applications, 2nd Edition. John Wiley,New York, 1973 .

ShalabhandWan, A.T.K., Stein-rule estimation in mixed regression models, Biometrical Journal, 42, (2000) 203-214.

Sutradhar, B.C.andAli, M.M., Estimation of parameters of regression with a Multivariate t-error variable, Communication Statistics - Theory and Methods, A 15, (1986), 429-450.

Sutradhar, B.C., Testing Linear Hypothesis with t - Error Variable, Sankhya: The Indian Journal of Statistics, Series B (1960-2002), 50(2), (1988), 175-180.

Theil, H., Principles of Econometrics, Vol. 1. Wiley, New York, 1971.

Zellner, A., Bayesian and non-Bayesian analysis of regression model with multivariate t-error terms, Journal of the American Statistical Association, 71, (1976), 400-405




DOI: http://dx.doi.org/10.23755/rm.v32i0.330

Refbacks

  • There are currently no refbacks.


Copyright (c) 2017 Manoj Kumar et al.

Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.

Ratio Mathematica - Journal of Mathematics, Statistics, and Applications. ISSN 1592-7415; e-ISSN 2282-8214.