Status : Verified
| Personal Name | Ferrer, Jeremiah B. |
|---|---|
| Resource Title | EBLUP Random Effects Augmentation for Zero-Sample Areas with Application to Philippine Rice Production Data |
| Date Issued | May 2026 |
| Abstract | The Fay-Herriot (FH) model is widely used in small area estimation for producing reliable domain-level estimates by combining direct survey data with auxiliary infor-mation through area-specific random effects. A well-known limitation of the EBLUP under this framework, however, is that it requires a direct survey estimate to compute the shrinkage factor. When certain areas have zero sample sizes, the EBLUP collapses to the synthetic estimator, effectively setting the area-specific random effect to zero, and loses the ability to reflect between-area heterogeneity. This study asks: when the EBLUP can no longer recover a random effect for a zero-sample area, can the available cluster or spatial structure in the data be used to construct a reasonable proxy? This study proposes Random Effects Augmentation (REA), a two-stage approach that improves zero-sample estimation while retaining the classical FH structure. In the first stage, the standard FH model is fitted to sampled areas to obtain FH-shrunken random effects. In the second stage, proxy random effects for zero-sample areas are constructed by averaging these shrunken effects within a local reference group under a local exchangeability assumption. Two variants are developed: REA-ML, which uses covariate-defined groups, and REA-SP, which uses geographic proximity or adminis-trative boundaries. The methods are evaluated in a simulation study with 100 areas across 13 scenarios varying structure type, structure strength, and zero-sample proportion. Results show that REA offers little gain when cluster or spatial structure is weak, provides meaningful im-provement over the synthetic estimator when structure is moderate, and is outperformed by dedicated multilevel or spatial models when structure is strong. The methods are also applied to Philippine rice production data for 81 provinces under 20%, 30%, and 40% zero-sample settings. Overall, REA provides a practical middle-ground approach for improving zero-sample predic |
| Degree Course | Master of Science (Statistics) |
| Language | English |
| Keyword | Small area estimation, Fay-Herriot model, EBLUP, Zero-sample areas, spatial statistics, Multilevel models |
| Material Type | Thesis/Dissertation |
Preliminary Pages
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Category : F - Regular work, i.e., it has no patentable invention or creation, the author does not wish for personal publication, there is no confidential information.
Access Permission : Open Access