Semiparametric Marginal Models For Incomplete Binary Longitudinal Data With Dropouts
Bibliographic record
Abstract
In this thesis, we explore semiparametric marginal models for binary longitudinal data with dropouts.We are specifically interested in the joint estimation of the marginal mean parameters and association parameters by second order generalized estimating equations when the marginal mean response model is partially linear.First, we propose and explore a set of weighted generalized estimating equations (GEEs) for fitting regression models to longitudinal binary responses when there are dropouts.Under a given missing data mechanism, the proposed method provides unbiased estimators of the regression parameters and association parameters.Simulations were carried out to study the robustness properties of the proposed method under both correctly specified and misspecified correlation structures.The method is also illustrated in an analysis of some actual incomplete longitudinal data on cigarette smoking trends, which were used to study coronary artery development in young adults.We also developed a semiparametric approach to analyzing longitudinal binary data.We applied second order GEE approach to analyze longitudinal binary responses under partially linear single-index models.We use the local polynomial smoothing technique to estimate the single-index parameters.We study the empirical properties of the proposed method in simulations.Our simulation study demonstrates that if the true underlying model is partially linear, then our proposed consciously, how good statistical theory and practice is done.I appreciate all his contributions of time, ideas, and funding to make my Ph.D. experience productive and stimulating.The joy and enthusiasm he has for his research was contagious and motivational for me, even during tough times in the Ph.D. pursuit.I am also thankful for the excellent example he has provided as a successful Bangladeshi-Canadian statistician and professor.
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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 | 0.023 | 0.087 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.005 | 0.004 |
| Research integrity | 0.002 | 0.005 |
| 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".