A Class of Two-Sample Nonparametric Tests for Panel Count Data
Bibliographic record
Abstract
Panel count data frequently occur in many situations including medical follow-up studies and reliability experiments. For two-sample comparison based on panel count data, several procedures have been proposed including Thall and Lachin (1988 Thall , P. F. , Lachin , J. M. ( 1988 ). Analysis of recurrent events: nonparametric methods for random-interval count data . J. Amer. Statist. Assoc. 83 : 339 – 347 .[Taylor & Francis Online], [Web of Science ®] , [Google Scholar]) and Sun and Fang (2003 Sun , J. , Fang , H. B. ( 2003 ). A nonparametric test for panel count data . Biometrika 90 : 199 – 208 .[Crossref], [Web of Science ®] , [Google Scholar]). In this article, a new class of nonparametric test procedures are presented. The test is a generalization of that for the same problem for failure time data and overcomes some shortcomings of the existing methods. Monte Carlo simulation studies are conducted to evaluate the presented approach and suggest that it works well. An illustrative example is discussed.
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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 | 0.027 | 0.119 |
| Meta-epidemiology (narrow) | 0.002 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.004 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.003 | 0.006 |
| Insufficient payload (model declined to judge) | 0.006 | 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".