Some Improved Mixed Regression Estimators and their Comparison when Disturbance Terms follow Multivariate T-Distribution
Abstract
Keywords
Full Text:
PDFReferences
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.
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.