Supervised network prediction for household statistics
Supervised network prediction for household statistics
Producing census-like household statistics is possible based on relevant data originated from the administrative sources. However, address registration errors generally cause biased results. Under the assumption that households can be identified by survey at a sample of addresses, we develop a supervised network prediction approach to households at the out-of-sample addresses. An application to real data from the Norwegian Household Register is used to demonstrate the advantages of the proposed modelling approach over the existing practical alternatives.
network modelling, community detection, graph representation learning, spectral embeddings, prediction accuracy
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
10 November 2025
Zhang, Li-Chun
a5d48518-7f71-4ed9-bdcb-6585c2da3649
Zhang, Li-Chun
(2025)
Supervised network prediction for household statistics.
Journal of the Royal Statistical Society: Series A (Statistics in Society).
(doi:10.1093/jrsssa/qnaf181).
Abstract
Producing census-like household statistics is possible based on relevant data originated from the administrative sources. However, address registration errors generally cause biased results. Under the assumption that households can be identified by survey at a sample of addresses, we develop a supervised network prediction approach to households at the out-of-sample addresses. An application to real data from the Norwegian Household Register is used to demonstrate the advantages of the proposed modelling approach over the existing practical alternatives.
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SNP-hh-r2
- Accepted Manuscript
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qnaf181
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Accepted/In Press date: 13 October 2025
Published date: 10 November 2025
Keywords:
network modelling, community detection, graph representation learning, spectral embeddings, prediction accuracy
Identifiers
Local EPrints ID: 506829
URI: http://eprints.soton.ac.uk/id/eprint/506829
ISSN: 0964-1998
PURE UUID: d3979bf8-2e78-4678-adf1-d3ba15d346cd
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Date deposited: 18 Nov 2025 18:11
Last modified: 19 Nov 2025 02:45
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