Bibliographic record
Abstract
In Small Area Estimation (SAE), the usual practice is to assume that the random components follow the normal distribution.A simulation study showed that under the one-fold nested error regression model assuming normal distribution may lead to large bias and significant increase of the mean squared error (MSE) of complex parameters (nonlinear function of the mean) estimation when the unit level errors' distribution is skewed.Hence, in this thesis, the assumption of normal distribution for the random components is relaxed by considering the skew-normal (SN) class of distributions.The SN class of distributions is very interesting because it contains the normal distribution family as a special case (when the skewness parameter is equal to zero).For the one-fold nested error regression model with the random components following SN distribution, the empirical best linear unbiased prediction (EBLUP) and the empirical best (EB) estimators of linear parameters are provided.Under the same model, EB estimators of complex parameters are developed following the approach introduced by Molina and Rao (2010).A simpler conditional alternative method is compared to the previously mentioned method.The semi-parametric method developed by Elbers et al. (2003) is improved by correctly assigning the area effects.A parametric bootstrap approach for the MSE estimation of the EB estimator and a semi-parametric bootstrap method for the ELL method are specified.An HB method for estimating complex parameters is also presented.The HB method uses Monte Carlo (MC) simulations and sampling importance resampling (SIR) techniques no
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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 | 0.013 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".