L-moments of asymmetric generalized distributions obtained through quantile splicing
Bibliographic record
Abstract
Balakrishnan et al. (Communications in Statistics Simulation and Computation 46 (2017) 4082–4097) proposed a skew logistic distribution by making use of the cumulative distribution function (CDF) of the folded logistic distribution. They made use of moments of order statistics from the standard folded logistic distribution to obtain the single and product moments of order statistics from the skew logistic distribution. Subsequently, Mac’Oduol et al. (Communications in Statistics—Theory and Methods 49 (2020) 4413–4429) proposed quantile splicing for the construction of two-piece distributions using quantile functions of symmetric distributions as building blocks. This paper presents the derivation of a general formula for the L-moments of such two-piece distributions. In addition, quantile splicing and its results are then specialized to the Tukey lambda distribution, and an example is used to illustrate the results developed.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".