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- W1946032234 abstract "In this chapter, the authors apply the results of empirical best linear unbiased prediction (EBLUP) they obtained to the basic area level, also called the Fay-Herriot (FH) model, because it was first proposed by Fay and Herriot. The chapter considers the basic area level model and spells out EBLUP estimation for the general linear mixed model (MM) with block diagonal covariance structure. It covers second-order unbiased mean squared error (MSE) estimation. Several practical issues associated with the FH model are studied and methods that address those issues are presented. The MSE and the MSE estimators are unconditional in the sense that they are valid under the model obtained by combining the sampling model and the linking model. The chapter describes methods for robust estimation of small area parameters, under the FH model with normality, in the presence of outliers in the random area effects, or the sampling errors, or both." @default.
- W1946032234 created "2016-06-24" @default.
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- W1946032234 date "2015-08-14" @default.
- W1946032234 modified "2023-10-16" @default.
- W1946032234 title "Empirical Best Linear Unbiased Prediction (EBLUP): Basic Area Level Model" @default.
- W1946032234 doi "https://doi.org/10.1002/9781118735855.ch6" @default.
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