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] |
| Copyright of Canadian Journal of Forest Research is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | GreenFILE |
| FullText | Text: Availability: 0 |
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| Header | DbId: 8gh DbLabel: GreenFILE An: 193262295 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
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| Items | – Name: Title Label: Title Group: Ti Data: Model-based small-area estimation with area-effects for sampled and non-sampled domains. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Kangas%2C+Annika%22">Kangas, Annika</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> annika.kangas@luke.fi</i><br /><searchLink fieldCode="AR" term="%22Myllymäki%2C+Mari%22">Myllymäki, Mari</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Packalen%2C+Petteri%22">Packalen, Petteri</searchLink><relatesTo>2</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Canadian+Journal+of+Forest+Research%22">Canadian Journal of Forest Research</searchLink>. 4/27/2026, Vol. 56, p1-10. 10p. – Name: Subject Label: Subject Terms Group: Su Data: <searchLink fieldCode="DE" term="%22Random+effects+model%22">Random effects model</searchLink><br /><searchLink fieldCode="DE" term="%22Sampling+%28Process%29%22">Sampling (Process)</searchLink><br /><searchLink fieldCode="DE" term="%22Prediction+models%22">Prediction models</searchLink><br /><searchLink fieldCode="DE" term="%22Parameter+estimation%22">Parameter estimation</searchLink><br /><searchLink fieldCode="DE" term="%22Small+area+statistics%22">Small area statistics</searchLink><br /><searchLink fieldCode="DE" term="%22Clustering+algorithms%22">Clustering algorithms</searchLink><br /><searchLink fieldCode="DE" term="%22Independent+variables%22">Independent variables</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Canadian Journal of Forest Research is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1139/cjfr-2025-0310 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 10 StartPage: 1 Subjects: – SubjectFull: Random effects model Type: general – SubjectFull: Sampling (Process) Type: general – SubjectFull: Prediction models Type: general – SubjectFull: Parameter estimation Type: general – SubjectFull: Small area statistics Type: general – SubjectFull: Clustering algorithms Type: general – SubjectFull: Independent variables Type: general Titles: – TitleFull: Model-based small-area estimation with area-effects for sampled and non-sampled domains. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Kangas, Annika – PersonEntity: Name: NameFull: Myllymäki, Mari – PersonEntity: Name: NameFull: Packalen, Petteri IsPartOfRelationships: – BibEntity: Dates: – D: 27 M: 04 Text: 4/27/2026 Type: published Y: 2026 Identifiers: – Type: issn-print Value: 00455067 Numbering: – Type: volume Value: 56 Titles: – TitleFull: Canadian Journal of Forest Research Type: main |
| ResultId | 1 |
