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A model-based disaggregation method for estimation of adult competency / by Andreea L. Erciulescu, Weijia Ren, Jianzhu Li, Tom Krenzke, Leyla Mohadjer and Robert Fay

Author
Erciulescu, Andreea L.
Additional Titles
At head of title: Proceedings of Statistics Canada Symposium 2022 data disaggregation : building a more representative data portrait of society
Published
[Ottawa] : Statistics Canada = Statistique Canada, 2024.
Edition
[Cat. No.: CS11-522/2022-1-3E-PDF].
Physical Description
1 online resource (7 unnumbered pages) : maps, graphs
Additional Creators
Statistics Canada
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Summary
"Estimation at fine levels of aggregation is necessary to better describe society. Small area estimation model-based approaches that combine sparse survey data with rich data from auxiliary sources have been proven useful to improve the reliability of estimates for small domains. Considered here is a scenario where small area model-based estimates, produced at a given aggregation level, needed to be disaggregated to better describe the social structure at finer levels. For this scenario, an allocation method was developed to implement the disaggregation, overcoming challenges associated with data availability and model development at such fine levels"--Abstract.
Report Numbers
CS11-522/2022-1-3E-PDF
Subject(s)
Note
Distributed by the Government of Canada Publishing and Depository Services Program (Weekly acquisitions list 2024-35).
Cover title.
Issued also in French under title: Méthode de désagrégation fondée sur un modèle pour l'estimation des compétences des adultes.
Other Edition
Erciulescu, Andreea L. [Cat. No.: CS11-522/2022-1-3F-PDF] [Ottawa] : Statistique Canada = Statistics Canada, le 25 mars 2024
Bibliography Note
Includes bibliographical references.
View MARC record | catkey: 46454465