EBLUP: Empirical best linear unbiased estimation**

Stephen Haslett; Alan Welsh

doi:10.1002/9781118445112.stat08180

EBLUP: Empirical best linear unbiased estimation**

Stephen Haslett, Alan Welsh

Research output: Chapter in Book/Report/Conference proceeding › Entry for encyclopedia/dictionary › peer-review

Abstract

In this article, we outline the basic properties of empirical best linear unbiased predictors (EBLUPs) and discuss some of the issues that arise in estimating their prediction mean squared errors. We introduce EBLUPs in the context of the linear mixed model with unknown covariances and briefly describe some of their applications. We then consider their particular application to small area estimation and outline ways to estimate the prediction mean squared error that have been developed in this context.

Original language	English
Title of host publication	Wiley StatsRef: Statistics Reference Online
Editors	Wiley StatsRef
Place of Publication	online
Publisher	John Wiley & Sons Ltd.
Pages	1-7
Volume	8
DOIs	https://doi.org/10.1002/9781118445112.stat08180
Publication status	Published - 2019

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10.1002/9781118445112.stat08180

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abstract = "In this article, we outline the basic properties of empirical best linear unbiased predictors (EBLUPs) and discuss some of the issues that arise in estimating their prediction mean squared errors. We introduce EBLUPs in the context of the linear mixed model with unknown covariances and briefly describe some of their applications. We then consider their particular application to small area estimation and outline ways to estimate the prediction mean squared error that have been developed in this context.",

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EBLUP: Empirical best linear unbiased estimation**

Abstract

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