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Smoothing County-Level Sampling Variances to Improve Small Area Models’ Outputs
Lu Chen, Luca Sartore, Habtamu Benecha, Valbona Bejleri, Balgobin Nandram. Stats, 2022-09-11
Topic: Risk, prevention and care in population studies
The use of hierarchical Bayesian small area models, which take survey estimates along with auxiliary data as input to produce official statistics, has increased in recent years. Survey estimates for small domains are usually unreliable due to small sample sizes, and the corresponding sampling variances can also be imprecise and unreliable. This affects the performance of the model (i.e., the model will not produce an estimate or will produce a low-quality modeled estimate), which results in a reduced number of official statistics published by a government agency. To mitigate the unreliable sampling variances, these survey-estimated variances are typically modeled against the direct estimates wherever a relationship between the two is present. However, this is not always the case. This paper explores different alternatives to mitigate the unreliable (beyond some threshold) sampling variances. A Bayesian approach under the area-level model set-up and a distribution-free technique based on bootstrap sampling are proposed to update the survey data. An application to the county-level corn yield data from the County Agricultural Production Survey of the United States Department of Agriculture’s (USDA’s) National Agricultural Statistics Service (NASS) is used to illustrate the proposed approaches. The final county-level model-based estimates for small area domains, produced based on updated survey data from each method, are compared with county-level model-based estimates produced based on the original survey data and the official statistics published in 2016.
Key findings
- This affects the performance of the model (i.e., the model will not produce an estimate or will produce a low-quality modeled estimate), which results in a reduced number of official statistics published by a government agency.
- The final county-level model-based estimates for small area domains, produced based on updated survey data from each method, are compared with county-level model-based estimates produced based on the original survey data and the official statistics published in 2016.
Why it matters
This study is about statistical methods for county-level crop yield estimates, not Alzheimer's disease, so it does not provide information on treating, curing or preventing Alzheimer's. It proposes ways to handle unreliable sampling variances in small area models.
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Study details
- Evidence strengthLow
How this study scored 28/100
Published in a peer-reviewed journal. Scored zero for study design, sample size, effect reported, studied in people and randomised / blinded because the record does not state it.
| Component | Points | Why |
|---|---|---|
| Journal tier | 7 / 10 | Tier 3 — peer-reviewed journal (Stats) |
| Journal's citation record | 0 / 4 | Stats: journal h-index 21 (OpenAlex, 2026-10-29) |
| 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 | 10 / 10 | Peer-reviewed. Published as an article in a named peer-reviewed journal. |
| How recent | 3 / 5 | 2022 — within 6 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 2 times (OpenAlex, 2026-10-10) |
| Citations per year | 0 / 5 | 0.3 citations a year over 6 year(s) since 2022 |
| Impact for its field and year | 1 / 6 | Cited 0.4× the average for papers of its field and year (OpenAlex FWCI) |
| Cited by studies here | 0 / 6 | No other study in this library cites it |
| Still being cited | 0 / 4 | Cited 2 times in 2024–2026 (2026 still in progress; OpenAlex) |
| Senior author's track record | 1 / 4 | Last-listed author Balgobin Nandram: h-index 16 (OpenAlex, whole career) |
| Institution's track record | 1 / 3 | United States Department of Agriculture: institution h-index 890 (OpenAlex) |
| Multi-centre collaboration | 2 / 3 | OpenAlex lists 4 institution(s) in 1 country |
| Indexed in PubMed | 1 / 2 | Not in PubMed, but has a DOI |
| Free to read | 0 / 2 | Behind a paywall — scores zero |
| Total | 28 / 100 | — |
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Read the original
Full text: free to read — OpenAlex lists this copy as free to read: the link above (checked 6 Feb 1020).
Cite this study APA, Vancouver, BibTeX · download .bib or .ris
Built from the record alzhub holds for this paper, not looked up. Volume, issue and page numbers are not held in the record. Check the citation against the original before you use it.
APA 7th
Chen, L., Sartore, L., Benecha, H., Bejleri, V., & Nandram, B. (2022). Smoothing County-Level Sampling Variances to Improve Small Area Models’ Outputs. Stats. https://doi.org/10.3390/stats5030052
Vancouver
Chen L, Sartore L, Benecha H, Bejleri V, Nandram B. Smoothing County-Level Sampling Variances to Improve Small Area Models’ Outputs. Stats. 2022. doi:10.3390/stats5030052.
BibTeX
@article{chen2022-1dd35578,
author = {Chen, Lu and Sartore, Luca and Benecha, Habtamu and Bejleri, Valbona and Nandram, Balgobin},
title = {{Smoothing County-Level Sampling Variances to Improve Small Area Models’ Outputs}},
journal = {Stats},
year = {2022},
doi = {10.3390/stats5030052},
url = {https://doi.org/10.3390/stats5030052}
}
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