Bibliographic record
Abstract
Correlated data collected from probability based sample surveys are often used in research studies in economics, health and social sciences.These surveys usually involve complex design such as stratification, clustering and unequal selection probability.Ignoring the correlations or the sampling design features may lead to erroneous inferences.In this thesis, we consider regression analysis of correlated survey data, taking account of both the correlation and sampling design.In the non-survey context, marginal models and mixed effects models are two approaches commonly used for correlated data.The Generalized Estimating Equation (GEE) method is the main method for marginal models and likelihood based methods are often used for mixed effects models.Recent progresses have been made to both approaches.Qu, Lindsay and Li (2000) proposed a quadratic inference functions (QIF) approach for marginal models that improves the GEE in terms of efficiency under misspecification of the second moment, and also possesses other features that the GEE does not.Lindsay (1988) proposed composite likelihood (CL) approach for multi-level mixed effects models.The CL method has been developed to reduce high dimensional likelihood functions to low dimensional ones, which makes the computation simpler while still having many of the good inference properties of a full List of Tables 1 SRE after 1,000 simulation, N = 80, T = 10.GEE Fixed: GEE with R being replaced by R * under unstructured working correlation, GEE UN: GEE under unstructured working correlation, GEE EX: GEE under exchangeable working correlation, GEE AR1: GEE under AR1 working correlation, QIF EX:
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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 | 0.070 | 0.330 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.003 | 0.007 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.006 | 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".