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VOL. 45 | 2004 On the strong consistency, weak limits and practical performance of the ML estimate and Bayesian estimates of a symmetric domain in $R^k$


This paper considers a problem of estimating an unknown symmetric region in $R^k$ based on $n$ points randomly drawn from it. The domain of interest is characterized by two parameters: size parameter $r$ and shape parameter $p$. Three methods are investigated which are the maximum likelihood, Bayesian procedures, and a composition of these two. A modification of Wald's theorem as well as a Bayesian version of it are given in this paper to demonstrate the strong consistency of these estimates. We use the measures of symmetric differences and the Hausdorff distance to assess the performance of the estimates. The results reveal that the composite method does the best. Discussion on the convergence in distribution is also given.


Published: 1 January 2004
First available in Project Euclid: 28 November 2007

zbMATH: 1268.62031
MathSciNet: MR2126905

Digital Object Identifier: 10.1214/lnms/1196285398

Primary: 62F10 , 62F12 , 62F15

Keywords: ball , Bayes , convex , domain , Euclidean space , extreme value , maximum likelihood , simulation , strong consistency , weak limit

Rights: Copyright © 2004, Institute of Mathematical Statistics


Vol. 45 • 1 January 2004
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