EDF‐based goodness‐of‐fit tests for ranked‐set sampling
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Parametric statistical procedures based on ranked‐set sampling are sensitive both to departures from the parametric family and to departures from perfect rankings. In this paper, we develop goodness‐of‐fit tests that are sensitive to departures of both types. These tests are modelled on the Kolmogorov–Smirnov and Cramér–von Mises goodness‐of‐fit tests for simple random sampling, and they take advantage of the fact that under perfect rankings, the cumulative distribution functions (CDFs) for the judgment order statistics are deterministic functions of the population CDF. We consider multiple ways of combining information across the judgment strata, and we find that summing or taking the maximum of separate stratum‐by‐stratum test statistics seems to give the best power. We prove that the best of the proposed tests are consistent against all alternatives. The Canadian Journal of Statistics 42: 451–469; 2014 © 2014 Statistical Society of Canada
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.
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 | 0.000 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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