Adjustments to the signed likelihood root and analysis of an embedded experiment in a survey
Bibliographic record
Abstract
This thesis consists of two projects. The first project is to develop an adjustment to the signed likelihood root (r) so that the normal approximation to the distribution of the adjusted r is improved. By using Taylor series expansions, we have developed an additive adjustment to r, which leads to a second-order approximation to its distribution. The theory is developed, simulations are recorded to indicate repetition accuracy, real data is analyzed, and connections to alternatives are discussed. The second project is dedicated to the analysis of an embedded experiment in a survey. We derive the Horvitz-Thompson estimator of the average treatment effect and its variance for a general design. Five estimators of corresponding variance are proposed and examined under a design combination of simple random sampling without replacement and completely randomized design. In the presence of auxiliary information, a new model-assisted estimator for the average treatment effect is developed and the variance of the estimator is derived. We show that the new estimator is approximately design-unbiased when a general model is employed to incorporate the auxiliary information. Moreover, it doesn't require auxiliary variable information at the population level and is relatively easy to implement and compute. Simulations carried out indicate that the new estimator gains in efficiency and its relative bias is negligible. Reliable variance estimators based on simulation experiments are suggested. The method proposed is applied to a synthetic data provided by Statistics Canada with multiple treatments under a design combination of stratified random sampling and randomized block design.
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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 | 0.028 | 0.146 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".