An empirical Bayes derivation of best linear unbiased predictors
type: Item
instance of: scholarly article
title: An empirical Bayes derivation of best linear unbiased predictors
zbMATH Open document ID: 1051.62008
DOI: 10.1155/S016117120211009X
author: Rohana J. Karunamuni
published in: International Journal of Mathematics and Mathematical Sciences
publication date: 2002-10-29T00:00:00Z
full work available at URL: https://eudml.org/doc/51066
review text: Summary: Let \((Y_{1},\theta _{1}),\dots, (Y_{n},\theta_{n})\) be independent real-valued random vectors with \(Y_{i}\), given \(\theta_{i}\), distributed according to a distribution depending only on \(\theta_{i}\) for \(i=1,\dots,n\). Best linear unbiased predictors (BLUPs) of the \(\theta_i\)'s are investigated. We show that BLUPs of \(\theta_i\)'s do not exist in certain situations. Furthermore, we present a general empirical Bayes technique for deriving BLUPs.
Mathematics Subject Classification ID: 62C12
Mathematics Subject Classification ID: 62H12
Mathematics Subject Classification ID: 62J05
zbMATH DE Number: 1821313
MaRDI profile type: Publication
OpenAlex ID: W2066940735
description: scientific article; zbMATH DE number 1821313