GENERALIZED ADDITIVE MIXED MODELS FOR SMALL AREA ESTIMATION
Bibliographic record
Abstract
Small Area Estimation (SAE) is a statistical technique to estimate parameters of sub-population containing small size of samples with adequate precision. This technique is very important to be developed due to the increasing needs of statistic for small domains, such as districts or villages. Some SAE techniques have been developed in Canada, USA, and UE based on real data. We adapted this technique to produce small area statistic in Indonesia based on national data collected by the Statistics Indonesia (Badan Pusat Statistik). We found that the linear model applied to auxiliary data produced estimates with low precision. In this paper we propose a class of generalized additive mixed model to improve the model of auxiliary data in small area estimation. Another method which can be used to obtain higher precision in small area estimation may be developed by linking some information in particular area with some other areas through appropriate model. This procedure is called indirect estimation. The procedure involves data from other domains. In other words, small area estimation model is borrowing strength from sample observation of related areas through auxiliary data (recent census and current administrative records) to increase effective sample size (Rao, 2003). In this paper we will discuss small area estimation through indirect method or estimation based models. One of the problems found in using this procedure is low precision of linear model for modeling of auxiliary data. In this paper we propose a class of generalized additive mixed model to improve the model of auxiliary data in small area estimation. This paper also presents application on small area estimation using poverty data from Susenas 2005 and Podes 2005 at Bogor District in West Java.
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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 | 0.015 | 0.034 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.006 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.012 | 0.003 |
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".