Small Area Estimation Methodology (SAE) applied on Bogota Multipurpose Survey (EMB)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it