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Small-Area Estimation with Spatial Similarity
Nicholas T. Longford. Not indexed, 2008
Topic: Risk, prevention and care in population studies
We derive a class of composite estimators of small-area quantities that exploit spatial (distance-related) similarity. They are based on a distribution-free model for the areas, but the estimators are aimed to have optimal design-based properties. Composition is applied also to estimating some of the global parameters on which the small-area estimators depend. We show that the commonly adopted assumption of random effects is not necessary for exploiting the similarity of the districts (borrowing strength across the districts). The methods are applied to estimation of the mean household sizes and the proportions of single-member households in the counties (co-marcas) of Catalonia.
Key findings
- We derive a class of composite estimators of small-area quantities that exploit spatial (distance-related) similarity.
- They are based on a distribution-free model for the areas, but the estimators are aimed to have optimal design-based properties.
- Composition is applied also to estimating some of the global parameters on which the small-area estimators depend.
- We show that the commonly adopted assumption of random effects is not necessary for exploiting the similarity of the districts (borrowing strength across the districts).
- The methods are applied to estimation of the mean household sizes and the proportions of single-member households in the counties (co-marcas) of Catalonia.
Why it matters
This paper develops statistical methods for estimating small-area quantities by borrowing strength from nearby areas. It provides no information on treating, curing or preventing Alzheimer's disease.
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Study details
- Evidence strengthLow
How this study scored 15/100
Preprint — not yet peer-reviewed. It scored 15 on the same rubric, which is below the 16 cap we apply to non-peer-reviewed sources. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it. We have no data on impact for its field and year, so that was left out: it earned 14 of the 94 points we could judge it on, which is 15 out of 100.
| Component | Points | Why |
|---|---|---|
| Journal tier | 2 / 10 | Tier 4 — not a peer-reviewed journal (Not indexed) |
| Journal's citation record | 0 / 4 | Not a journal article, so the journal's record does not count |
| Study design | 0 / 12 | No study design recorded — scores zero, not guessed (not stated in the record — scored 0) |
| Strength of evidence | 1 / 5 | Rated “low” in our summary |
| Peer-review validation | 4 / 10 | Preprint — not yet peer-reviewed. Hosted on a preprint server. |
| How recent | 1 / 5 | 2008 — within 20 year(s) of the newest study here |
| Sample size | 0 / 4 | Participant count not stated — scores zero (not stated in the record — scored 0) |
| Effect reported | 0 / 3 | No effect size reported — scores zero (not stated in the record — scored 0) |
| Studied in people | 0 / 3 | Not stated whether this was studied in people — scores zero (not stated in the record — scored 0) |
| Randomised / blinded | 0 / 2 | No randomisation or blinding stated — scores zero (not stated in the record — scored 0) |
| Times cited | 1 / 7 | Cited 1 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.1 citations a year over 20 year(s) since 2008 |
| Impact for its field and year | — / 6 | No field-weighted citation impact (OpenAlex has none yet) — left out of this score |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 0 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 2 / 4 | Last-listed author Nicholas T. Longford: h-index 30 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | Universitat Pompeu Fabra: institution h-index 747 (OpenAlex) |
| Multi-centre collaboration | 1 / 3 | OpenAlex lists 2 institution(s) in 2 countries |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Points | 14 / 94 | Scored on 19 of 20 parts. We have no data for the rest, so they are left out: 14 of the 94 points we could judge it on is 15 out of 100. |
| Total | 15 / 100 | — |
What each of these 20 components measures, and what this score is not →
Read the original
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Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. This record has no journal, so the citation leaves it out. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Longford, N. T. (2008). Small-Area Estimation with Spatial Similarity. https://doi.org/10.2139/ssrn.1285451
Vancouver
Longford NT. Small-Area Estimation with Spatial Similarity. 2008. doi:10.2139/ssrn.1285451.
BibTeX
@article{longford2008-1b628aad,
author = {Longford, Nicholas T.},
title = {{Small-Area Estimation with Spatial Similarity}},
year = {2008},
doi = {10.2139/ssrn.1285451},
url = {https://doi.org/10.2139/ssrn.1285451}
}