Missing Responses in Generalized Linear Mixed Models Where the Missingness is Nonignorable
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.This thesis also reviews generalized linear mixed models (GLMMs) and methods to find the maximum likelihood estimators of both fixed and random effects parameters, where GLMMs are of increasing importance to many practitioners.GLMMs are widely used in clustered and longitudinal data analyses, where random effects are used to model subject or cluster specific effects.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 Graduate Administrator of the school, and Kevin Crosby, the School Administrator for their valuable
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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 | 0.053 | 0.188 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.006 | 0.004 |
| Research integrity | 0.005 | 0.009 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".