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ON THE USE OF THE STEIN VARIANCE ESTIMATOR IN THE DOUBLE k-CLASS ESTIMATOR IN REGRESSION

  • Kazuhiro Ohtani
  • Alan Wan

This paper investigates the predictive mean squared error performance of a modified double k-class estimator by incorporating the Stein variance estimator. Recent studies show that the performance of the Stein rule estimator can be improved by using the Stein variance estimator. However, as we demonstrate below, this conclusion does not hold in general for all members of the double k-class estimators. On the other hand, an estimator is found to have smaller predictive mean squared error than the Stein variance-Stein rule estimator, over quite large parts of the parameter space.

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File URL: http://www.tandfonline.com/doi/abs/10.1081/ETC-120008726
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Article provided by Taylor & Francis Journals in its journal Econometric Reviews.

Volume (Year): 21 (2002)
Issue (Month): 1 ()
Pages: 121-134

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Handle: RePEc:taf:emetrv:v:21:y:2002:i:1:p:121-134
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  1. Carter, R. A. L., 1981. "Improved Stein-rule estimator for regression problems," Journal of Econometrics, Elsevier, vol. 17(1), pages 113-123, September.
  2. Ohtani, Kazuhiro, 1996. "Further improving the Stein-rule estimator using the Stein variance estimator in a misspecified linear regression model," Statistics & Probability Letters, Elsevier, vol. 29(3), pages 191-199, September.
  3. Vinod, H. D., 1980. "Improved stein-rule estimator for regression problems," Journal of Econometrics, Elsevier, vol. 12(2), pages 143-150, February.
  4. Ohtani, Kazuhiro, 1988. "Optimal levels of significance of a pre-test in estimating the disturbance variance after the pre-test for a linear hypothesis on coefficients in a linear regression," Economics Letters, Elsevier, vol. 28(2), pages 151-156.
  5. Ullah, Aman & Ullah, Shobha, 1978. "Double k-Class Estimators of Coefficients in Linear Regression," Econometrica, Econometric Society, vol. 46(3), pages 705-22, May.
  6. Gelfand, Alan E. & Dey, Dipak K., 1988. "Improved estimation of the disturbance variance in a linear regression model," Journal of Econometrics, Elsevier, vol. 39(3), pages 387-395, November.
  7. Srivastava, V. K. & Chaturvedi, A., 1986. "A necessary and sufficient condition for the dominance of an improved family of estimators in linear regression models," Economics Letters, Elsevier, vol. 20(4), pages 345-349.
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