Bibliographic record
Abstract
In this thesis, we study statistical inference in the presence of missing data. In Chapters 2-4, we obtain asymptotically valid imputed estimators for the population mean, distribution function and correlation coefficient, and propose adjustments to Shao and Sitter (1996) bootstrap confidence intervals under imputation for missing data. We show that the adjusted bootstrap estimators should be used with bootstrap data obtained by imitating the process of imputing the original data set. In Chapter 5, we establish a goodness-of-fit test that can be applied to the case of longitudinal data with missing at random (MAR) observations, by combining the concepts of weighted generalized estimating equations (Robins et al., 1995) and score test statistic for goodness-of-fit (Hosmer and Lemeshow, 1980; Horton et al., 1999). We show that the proposed goodness-of-fit method that incorporates the missingness process should be used when dealing with intermittent missingness. In Chapter 6, we study a conditional model for a mixture of correlated, discrete and continuous, outcomes and apply the likelihood method to MAR data. We conduct a simulation study to compare the performance of estimators resulting from the joint model with estimators based on separate models for binary and continuous outcomes. We show that when all data are observed, adopting the mixed model does not lead to notable improvements; on the contrary, under a scenario with binary MAR data, the joint model performs significantly better.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.090 | 0.317 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.004 | 0.007 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.004 | 0.009 |
| Insufficient payload (model declined to judge) | 0.003 | 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".