Bibliographic record
Abstract
Abstract Without realizing the fact that the time‐dependent covariates corresponding to the repeated discrete responses under a generalized linear longitudinal model (GLLM) cause non‐stationary (time dependent) correlations for the repeated responses, many existing studies use a stationary (either “working” or true) correlation structure to develop certain estimating equations for the regression effects involved in the model. By constructing suitable non‐stationary correlation structures both for longitudinal count and binary data, this article first demonstrates that the stationary correlations based estimation approaches may yield inefficient regression estimates. For efficient estimation, the article then suggests a true non‐stationary correlation structure based generalized quasi‐likelihood (GQL) estimation approach, where non‐stationary correlation structure is identified by exploiting the estimated lag correlations of the responses. A generalization of the GLLM to the familial‐longitudinal set up both for count and binary data is also discussed, where the data exhibit familial as well as non‐stationary longitudinal correlations, the familial correlations among the responses of the family members are being generated through a random common family effect. The GQL estimating equations are provided for the estimation of the regression and the variance component parameters of this generalized linear longitudinal mixed model (GLLMM), whereas the longitudinal correlations are estimated by solving suitable moment estimating equations. The Canadian Journal of Statistics 38: 174–196; 2010 © 2010 Statistical Society of Canada
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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 | 0.085 | 0.329 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.007 | 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".