MSE Estimation in Transformed Fay-Herriot Models
نویسندگان
چکیده
The problem of accurately estimating the Mean Squared Error (MSE) of Small Area Estimators (SAE’s) within a Fay-Herriot (1979) normal-error model is studied theoretically in the common setting where the model is fitted to a logarithmically transformed response variable. For bias-corrected EBLUP small-area point estimators, MSE formulas and estimators are provided, with biases of smaller order than the reciprocal of the number of small areas. The performance of these MSE estimators is illustrated by a simulation study and two real-data examples, one relating to the county-level estimation of child poverty rates in the US Census Bureau’s ongoing Small Area Income and Poverty Estimation (SAIPE) project.
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