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Model-based small-area estimation with area-effects for sampled and non-sampled domains.

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Title: Model-based small-area estimation with area-effects for sampled and non-sampled domains.
Authors: Kangas, Annika1 (AUTHOR) annika.kangas@luke.fi, Myllymäki, Mari2 (AUTHOR), Packalen, Petteri2 (AUTHOR)
Source: Canadian Journal of Forest Research. 4/27/2026, Vol. 56, p1-10. 10p.
Subject Terms: Random effects model, Sampling (Process), Prediction models, Parameter estimation, Small area statistics, Clustering algorithms, Independent variables
Abstract: Previous studies recommend the empirical best linear unbiased predictor (EBLUP) for small-area estimation. However, EBLUP estimation requires at least one observation from each small area, while most of the areas may be non-sampled. One approach to overcome this problem is to predict the area-effects for the non-sampled areas with a model developed using the estimated area-effects from the sampled areas. Another approach is to cluster the small areas to larger groups and introduce a cluster-effect into the prediction model. We tested these approaches in a set of simulated small areas (domains). When observations from all or most domains were available, EBLUP with a domain-effect, or combined cluster- and domain-effect were the most reliable calibration methods. When the sampling fraction and the size of the domains were smaller, calibrating with the cluster-effect only was the most reliable method. Without any calibration, the model-based estimates for the domains with the highest volumes were severely underestimated. When observations were available, the EBLUP calibration improved the results in the high-end of the distribution. With the smallest sampling fractions and domains, also the predicted area-effects reduced the underestimation. However, the modelled area-effects were estimated from the population data, rather than from a sample. [ABSTRACT FROM AUTHOR]
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Database: GreenFILE
Description
Abstract:Previous studies recommend the empirical best linear unbiased predictor (EBLUP) for small-area estimation. However, EBLUP estimation requires at least one observation from each small area, while most of the areas may be non-sampled. One approach to overcome this problem is to predict the area-effects for the non-sampled areas with a model developed using the estimated area-effects from the sampled areas. Another approach is to cluster the small areas to larger groups and introduce a cluster-effect into the prediction model. We tested these approaches in a set of simulated small areas (domains). When observations from all or most domains were available, EBLUP with a domain-effect, or combined cluster- and domain-effect were the most reliable calibration methods. When the sampling fraction and the size of the domains were smaller, calibrating with the cluster-effect only was the most reliable method. Without any calibration, the model-based estimates for the domains with the highest volumes were severely underestimated. When observations were available, the EBLUP calibration improved the results in the high-end of the distribution. With the smallest sampling fractions and domains, also the predicted area-effects reduced the underestimation. However, the modelled area-effects were estimated from the population data, rather than from a sample. [ABSTRACT FROM AUTHOR]
ISSN:00455067
DOI:10.1139/cjfr-2025-0310