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A Spatial Fay-Herriot Model when the Auxiliary Variables are based on Non-traditional Data — arXiv:2608.09626

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.09626
License (as archived): CC0-1.0
Archived: 2026-10-19T09:03:29.432Z
SHA-256 of the archived text: c28e1e633c83cf63cea3dd6d42de44d0e731686dea057433ebbf8685e0537fac
Stable URL of this page: https://metraipolis.com/documento/dc_42e89e65d951

How to cite

A Spatial Fay-Herriot Model when the Auxiliary Variables are based on Non-traditional Data — arXiv:2608.09626 — arXiv (en), https://arxiv.org/abs/2608.09626 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_42e89e65d951 (Metraipolis archive dc_42e89e65d951, sha256 c28e1e633c83cf63cea3dd6d42de44d0e731686dea057433ebbf8685e0537fac)

BibTeX

@misc{dc42e89e65d951,
  title = {A Spatial Fay-Herriot Model when the Auxiliary Variables are based on Non-traditional Data — arXiv:2608.09626},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.09626},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_42e89e65d951; sha256 c28e1e633c83cf63cea3dd6d42de44d0e731686dea057433ebbf8685e0537fac},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.09626. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Small area estimation methods combine direct survey estimates with model-based predictions to produce reliable estimates of population quantities. When covariates are measured with error, as often occurs when auxiliary information is coming from big data sources, standard area-level approaches such as the traditional Fay-Herriot model can perform poorly if this measurement error is ignored, potentially yielding biased estimates. Building on the extension proposed by Ybarra and Lohr to account for measurement error in covariates, we further develop this modelling framework by incorporating spatial dependence between areas. We propose a spatial measurement error Fay-Herriot model that jointly accounts for covariate measurement error and spatial correlation, enabling information borrowing across relevant neighbouring areas while properly adjusting for the additional uncertainty introduced by the covariates. We derive the properties of the proposed model and its parameter estimators and outline a parametric bootstrap procedure for estimating the mean squared error of the resulting empirical best linear unbiased predictor. A simulation study examines model performance under a range of measurement error scenarios. The proposed approach is then applied to European Social Survey data, supplemented with big data auxiliary information, to estimate regional attitudes towards climate change in Spain.

Tema degli esploratori: statistica e scienza dei dati (retro-catalogo arXiv: annunci del 2026-08-11 in stat.ME).
Categorie arXiv: Methodology (stat.ME).
Pagina del lavoro: https://arxiv.org/abs/2608.09626 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-19T09:02:02.377Z dalla pagina https://arxiv.org/catchup/stat.ME/2026-08-11?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).