Asymptotics for arrays of martingale differences in recurrent event analysis
Why this work is in the frame
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Bibliographic record
Abstract
The study of recurrent events, such as hospital readmissions or stock market volatility spikes, is important in biostatistics and finance but poses statistical challenges due to complex dependencies and censoring. In this paper, we propose a semiparametric framework for analyzing recurrent event data, focusing on gap times between successive events. We develop estimating functions to sequentially estimate regression parameters, yielding estimators with desirable properties, while accommodating time-varying covariates and right-censoring. Unlike previous approaches, we establish a strong law of large numbers and a central limit theorem for stopped martingales under random, potentially unbounded stopping times, useful for inference in dynamic settings. We illustrate the relevance of our approach to the analysis of longitudinal data and autoregressive models.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.011 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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