SEMI-PARAMETRIC PREDICTION INTERVALS IN SMALL AREAS WHEN AUXILIARY DATA ARE MEASURED WITH ERROR
Gauri Datta, Aurore Delaigle, Peter Hall, Li Wang
STATISTICA SINICA | STATISTICA SINICA | Published : 2018
In recent years, demand for reliable small area statistics has considerably increased, but the size of samples obtained in small areas is too often small to produce accurate predictors of quantities of interest. To overcome this difficulty, a common approach is to use auxiliary data from other areas or other sources, and produce estimators that combine them with direct data. A popular model for combining direct and indirect data sources is the Fay-Herriot model, which assumes that the auxiliary variables are observed accurately. However, these variables are often subject to measurement errors, and not taking this into account can lead to estimators that are even worse than those based exclus..View full abstract
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Awarded by National Science Foundation
Delaigle and Hall's research was supported by grants and fellowships from the Australian Research Council. Wang's research was supported in part by National Science Foundation grants DMS-1106816 and DMS-1542332.