Asymptotic Improvement of the Usual Confidence Set in a Multivariate Normal Distribution with Unknown Variance
AbstractWe 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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Bibliographic InfoArticle provided by Elsevier in its journal Journal of Multivariate Analysis.
Volume (Year): 64 (1998)
Issue (Month): 2 (February)
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Web page: http://www.elsevier.com/wps/find/journaldescription.cws_home/622892/description#description
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- 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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- 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.
- Robert, Christian & Casella, George, 1990. "Improved confidence sets for spherically symmetric distributions," Journal of Multivariate Analysis, Elsevier, vol. 32(1), pages 84-94, January.
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