Inferences in median regression models for asymmetric longitudinal data: A quasi-likelihood approach
Why this work is in the frame
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Bibliographic record
Abstract
In the independence setup, when the responses exhibit high degree of asymmetry, the median regression model is preferred to the mean regression model to obtain consistent and efficient regression estimates. However, when this type of asymmetric data are collected repeatedly over time, developing median regression model for such correlated asymmetric data may not be easy. As a remedy, there exist some studies where the longitudinal correlations of this type of asymmetric data have been computed using the moment estimates for all pairwise correlations and these correlations of repeated (multi-dimensional) data used to develop a median based quasi-likelihood approach for estimation of the regression effects. By considering an autoregressive order 1 (AR(1)) model for longitudinal exponential responses, in this paper, it is however, demonstrated that the existing pairwise estimates of correlations under median regression model may yield inefficient estimates as compared to the simpler independence assumption based estimates. We illustrate the inference techniques discussed in the paper by reanalyzing the well-known labor pain data.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 it