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- W2481749144 abstract "Linear mixed models (LMMs) handle data in which observations are not independent. This chapter gives an introduction to area-level LMMs in the framework of small area estimation. The seminal Fay-Herriot model is the starting point for giving an overview on area-level spatio-temporal models. In addition, the chapter introduces a new temporal model with domain and timeMA (1)-correlated random effects u1, d and u2, dt, respectively. The new model takes into account cross sectional and temporal variability of direct estimators of domain parameters. The domain empirical best linear unbiased prediction (EBLUPs) and several mean square error (MSE) estimators are given and investigated by means of Monte Carlo simulations. The chapter also illustrates how to apply the new model to the estimation of domain poverty proportions. The applicability of the introduced area-level model to the estimation of small area parameters is the main message for applied statisticians." @default.
- W2481749144 created "2016-08-23" @default.
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- W2481749144 date "2016-01-01" @default.
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- W2481749144 title "Area‐level Spatio‐temporal Small Area Estimation Models" @default.
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- W2481749144 doi "https://doi.org/10.1002/9781118814963.ch11" @default.
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