Missing Responses in Generalized Linear Mixed Models Where the Missingness is Nonignorable
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
In this thesis, we go through some practical issues of binary and Poisson regression models, which are particular cases of generalized linear models (GLMs) and are very useful for analyzing real datasets. We review methods for finding the maximum likelihood estimators in GLMs. The treatment of such methods provides a rigid foundation of GLMs. Completion of this work would have been impossible without his guidance. He was very patient, and he encouraged me in a professional way to defeat my fears until I reached the completion of this work. It has been an honour to get a chance of being his M.Sc. student. I also thank many people in the School of Mathematics and Statistics at Carleton University for their help, continual support to continue my graduate study in Statistics. I would like to thank Nicole Gaertner, the
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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 | 0.001 | 0.001 |
| 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) | 0.002 | 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