Small Area Estimation Methodology (SAE) applied on Bogota Multipurpose Survey (EMB)
Bibliographic record
Abstract
Small Area Estimation Methodology (SAE) is a widely used by statistical offices in several countries to reduce sampling errors with the help of auxiliary information. Different countries such as USA, Canada, England, Israel and European Community have within their statistical institutes offices dedicated to the application of SAE in several investigations. So far, the National Administrative Department of Statistics of Colombia (DANE), has not published official statistics that involve this methodology. The present work illustrates the advantages in the use and estimation of living conditions using SAE. Formally, the unemployment rate and the average income levels of municipalities of Cundinamarca are estimated. For this purpose, information of the Multipurpose Survey 2014 is used and is complemented with socio-demographic and economic related auxiliary information. A mixed Fay & Herriot (1979) model it is used in order to get the estimates. We use R ecosystem to develop SAE methodology. R is used for data wrangling, model adjustment, parameter estimation and finally visualization with the aid of renowned packages such as tidyr, forcats, sae, ggplot2 among others. We will show R implementation and some remarkable results. First, a good adjustment of the model to the data; second, a reduction in the sampling errors reported by the estimation in small areas compared to the direct estimates generated by the Bogota Multipurpose Survey (EMB); and finally acceptable estimates for municipalities that were not covered by the survey.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.018 | 0.060 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.016 | 0.004 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".