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Shrinkage estimation in elliptically contoured distribution with restricted parameter space
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Hisayuki Tsukuma
Published/Copyright:
December 4, 2009
Abstract
This paper deals with the problem of estimating the mean vector of an elliptically contoured distribution with unknown scale matrix, where the mean vector is restricted to an unbounded and closed convex set with a smooth or a piecewise smooth boundary. A shrinkage-type estimator is shown to be better than the maximum likelihood estimator subject to the restriction relative to a quadratic loss.
Keywords: Divergence theorem; elliptically contoured distribution; quadratic loss; restricted maximum likelihood estimator; restricted parameter space
Published Online: 2009-12-04
Published in Print: 2009-11
© by Oldenbourg Wissenschaftsverlag, München, Germany
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Keywords for this article
Divergence theorem;
elliptically contoured distribution;
quadratic loss;
restricted maximum likelihood estimator;
restricted parameter space
Articles in the same Issue
- Robust efficient hedging for American options: The existence of worst case probability measures
- Shrinkage estimation in elliptically contoured distribution with restricted parameter space
- Minimum risk equivariant estimator in linear regression model
- Non-standard behavior of density estimators for sums of squared observations
- The likelihood ratio test for non-standard hypotheses near the boundary of the null – with application to the assessment of non-inferiority
