Asymptotic Improvement of the Usual Confidence Set in a Multivariate Normal Distribution with Unknown Variance
We consider confidence sets for the mean of a multivariate normal distribution with unknown covariance matrix of the form[sigma]2I. The coverage probability of the usual confidence set is shown to be improved asymptotically by centering at a shrinkage estimator.
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Volume (Year): 64 (1998)
Issue (Month): 2 (February)
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References listed on IDEAS
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- Robert, Christian & Casella, George, 1990. "Improved confidence sets for spherically symmetric distributions," Journal of Multivariate Analysis, Elsevier, vol. 32(1), pages 84-94, January.
- Hwang, J.T. & Ullah, A., 1989.
"Confidence Sets Centered At James-Stein Estimators- A Surprise Concerning The Unknown Variance Case,"
UWO Department of Economics Working Papers
8909, University of Western Ontario, Department of Economics.
- Hwang, J. T. Gene & Ullah, Aman, 1994. "Confidence sets centered at James--Stein estimators : A surprise concerning the unknown-variance case," Journal of Econometrics, Elsevier, vol. 60(1-2), pages 145-156.
- Ullah, A. & Hwang, J.T., 1991. ""Confidence Sets Centered at James-Stein Estimators--A Surprise Concerning the Unknown Variance Case"," The A. Gary Anderson Graduate School of Management 92-36, The A. Gary Anderson Graduate School of Management. University of California Riverside.
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