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Estimation of order-restricted means from correlated data

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Author Info
Shyamal D. Peddada
David B. Dunson
Xiaofeng Tan
Abstract

In many applications, researchers are interested in estimating the mean of a multivariate normal random vector whose components are subject to order restrictions. Various authors have demonstrated that the likelihood-based methodology may perform poorly under certain conditions for such problems. The problem is much harder when the underlying covariance matrix is nondiagonal. In this paper a simple iterative algorithm is introduced that can be used for estimating the mean of a multivariate normal population when the components are subject to any order restriction. The proposed methodology is illustrated through an application to human reproductive hormone data. Copyright 2005, Oxford University Press.

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File URL: http://hdl.handle.net/10.1093/biomet/92.3.703
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Publisher Info
Article provided by Oxford University Press for Biometrika Trust in its journal Biometrika.

Volume (Year): 92 (2005)
Issue (Month): 3 (September)
Pages: 703-715
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Handle: RePEc:oup:biomet:v:92:y:2005:i:3:p:703-715

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  1. Kulan Ranasinghe & Mervyn J. Silvapulle, 2008. "Semiparametric estimation of duration models when the parameters are subject to inequality constraints and the error distribution is unknown," Monash Econometrics and Business Statistics Working Papers 5/08, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
  2. Kulan Ranasinghe & Mervyn J. Silvapulle, 2008. "Semiparametric estimation of duration models when the parameters are subject to inequality constraints and the error distribution is unknown," Monash Econometrics and Business Statistics Working Papers 1/08, Monash University, Department of Econometrics and Business Statistics. [Downloadable!]
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