Metraipolis › archive › dc_d31da1656ada

Inferential Issues in Model-based Small Area Estimation: Some History and Selective Recent Advances — arXiv:2611.04281

Source: arXiv (en)
Original: https://arxiv.org/abs/2611.04281
License (as archived): CC0-1.0
Archived: 2026-11-04T18:58:03.382Z
SHA-256 of the archived text: 8f354b5c29aa13d17d5f3a0ba7b9cf1c53e59a08c7510ad6220c905299d1c1a6
Stable URL of this page: https://metraipolis.com/documento/dc_d31da1656ada

How to cite

Inferential Issues in Model-based Small Area Estimation: Some History and Selective Recent Advances — arXiv:2611.04281 — arXiv (en), https://arxiv.org/abs/2611.04281 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_d31da1656ada (Metraipolis archive dc_d31da1656ada, sha256 8f354b5c29aa13d17d5f3a0ba7b9cf1c53e59a08c7510ad6220c905299d1c1a6)

BibTeX

@misc{dcd31da1656ada,
  title = {Inferential Issues in Model-based Small Area Estimation: Some History and Selective Recent Advances — arXiv:2611.04281},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2611.04281},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_d31da1656ada; sha256 8f354b5c29aa13d17d5f3a0ba7b9cf1c53e59a08c7510ad6220c905299d1c1a6},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2611.04281. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Model-based Small Area Estimation (SAE) has seen rapid growth over the past 25 years or so due to the rapidly increasing demand for reliable small area estimators of totals, means and more complex area parameters. Direct area-specific estimators are either unreliable (due to small area-specific sample sizes) or not feasible (due to zero area-specific sample sizes). It is therefore necessary to borrow strength from related areas through implicit or explicit models linking related areas. We focus on explicit area-level and unit-level models. After a brief historical sketch of the evolution of SAE, we focus on selective recent developments (mostly after 2015). Developments before 2015 are mostly covered in the comprehensive Wiley book by Rao and Molina (2015). Under a basic area-level model, we consider semi-parametric and robust estimation of small area means under model misspecifications. Under a basic unit level model, methods for estimating complex parameters, such as poverty indicators, will be presented, including robust estimation in the presence of outliers. A comparison of area-level and unit-level models when unit level data are available will also be presented. Estimation methods studied include empirical best linear unbiased prediction (EBLUP), empirical best (EB) and hierarchical Bayes (HB).

Tema degli esploratori: statistica e scienza dei dati (elenco arXiv stat.ME, nuovi lavori del giorno).
Categorie arXiv: Methodology (stat.ME) ; Statistics Theory (math.ST).
Pagina del lavoro: https://arxiv.org/abs/2611.04281 — 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 2026-11-04T18:36:37.155Z dalla pagina https://arxiv.org/list/stat.ME/new (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).