Improving on Minimum Risk Equivariant and Linear Minimax Estimators of Bounded Multivariate Location Parameters

We propose improvements under squared error loss of the minimum risk equivariant and the linear minimax estimators for estimating the location parameter θ of a p-variate spherically symmetric distribution, with θ restricted to a ball of radius m centered at the origin. Our construction of explicit...

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Main Authors: Éric Marchand, Amir T. Payandeh Najafabadi
Format: Article
Language:English
Published: Instituto Nacional de Estatística | Statistics Portugal 2010-11-01
Series:Revstat Statistical Journal
Subjects:
Online Access:https://revstat.ine.pt/index.php/REVSTAT/article/view/93
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author Éric Marchand
Amir T. Payandeh Najafabadi
author_facet Éric Marchand
Amir T. Payandeh Najafabadi
author_sort Éric Marchand
collection DOAJ
description We propose improvements under squared error loss of the minimum risk equivariant and the linear minimax estimators for estimating the location parameter θ of a p-variate spherically symmetric distribution, with θ restricted to a ball of radius m centered at the origin. Our construction of explicit improvements relies on a multivariate version of Kubokawa’s Integral Expression of Risk Difference (IERD) method. Applications are given for univariate distributions, for the multivariate normal, and for scale mixture of multivariate normal distributions.
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spelling doaj.art-5f5fc359f13d487e9a1124a22cfea9b12022-12-22T04:02:49ZengInstituto Nacional de Estatística | Statistics PortugalRevstat Statistical Journal1645-67262183-03712010-11-018210.57805/revstat.v8i2.93Improving on Minimum Risk Equivariant and Linear Minimax Estimators of Bounded Multivariate Location ParametersÉric Marchand 0Amir T. Payandeh Najafabadi 1Université de SherbrookeShahid Beheshti University We propose improvements under squared error loss of the minimum risk equivariant and the linear minimax estimators for estimating the location parameter θ of a p-variate spherically symmetric distribution, with θ restricted to a ball of radius m centered at the origin. Our construction of explicit improvements relies on a multivariate version of Kubokawa’s Integral Expression of Risk Difference (IERD) method. Applications are given for univariate distributions, for the multivariate normal, and for scale mixture of multivariate normal distributions. https://revstat.ine.pt/index.php/REVSTAT/article/view/93decision theoryspherical symmetric distributionrestricted parameterminimum risk equivariant estimatorlinear minimax estimatordominating estimators
spellingShingle Éric Marchand
Amir T. Payandeh Najafabadi
Improving on Minimum Risk Equivariant and Linear Minimax Estimators of Bounded Multivariate Location Parameters
Revstat Statistical Journal
decision theory
spherical symmetric distribution
restricted parameter
minimum risk equivariant estimator
linear minimax estimator
dominating estimators
title Improving on Minimum Risk Equivariant and Linear Minimax Estimators of Bounded Multivariate Location Parameters
title_full Improving on Minimum Risk Equivariant and Linear Minimax Estimators of Bounded Multivariate Location Parameters
title_fullStr Improving on Minimum Risk Equivariant and Linear Minimax Estimators of Bounded Multivariate Location Parameters
title_full_unstemmed Improving on Minimum Risk Equivariant and Linear Minimax Estimators of Bounded Multivariate Location Parameters
title_short Improving on Minimum Risk Equivariant and Linear Minimax Estimators of Bounded Multivariate Location Parameters
title_sort improving on minimum risk equivariant and linear minimax estimators of bounded multivariate location parameters
topic decision theory
spherical symmetric distribution
restricted parameter
minimum risk equivariant estimator
linear minimax estimator
dominating estimators
url https://revstat.ine.pt/index.php/REVSTAT/article/view/93
work_keys_str_mv AT ericmarchand improvingonminimumriskequivariantandlinearminimaxestimatorsofboundedmultivariatelocationparameters
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