A cross-validation framework for small area estimation decomposes error to separate measurable bias from bounded unknowns, showing that leave-one-area-out methods can produce misleading model rankings while the new approach provides more robust comparisons and uncertainty measures.
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On cross-validation for small area estimators
A cross-validation framework for small area estimation decomposes error to separate measurable bias from bounded unknowns, showing that leave-one-area-out methods can produce misleading model rankings while the new approach provides more robust comparisons and uncertainty measures.