Ordering results for random maxima and minima from two dependent Kumaraswamy-generalized distributed samples
Bibliographic record
Abstract
Let {X1,…,XN1} and {Y1,…,YN2} be two sequences of interdependent heterogeneous samples, where for i=1,…,N1, Xi∼Kw−G(x,αi,γi;G) and for i=1,…,N2, Yi∼Kw−G(x,βi,δi;H), where G and H are baseline distributions in the Kumaraswamy-generalized model and N1 and N2 are two positive integer-valued random variables, independently of Xi′s and Yi′s, respectively. In this article, we establish several stochastic orders, such as usual stochastic, hazard rate, reversed hazard rate, dispersive and likelihood ratio orders between the random maxima (XN1:N1 and YN2:N2) and the random minima (X1:N1 and X1:N2), when the sample sizes are different and random (positive).
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 | 0.020 | 0.079 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.003 | 0.008 |
| Open science | 0.003 | 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".