My musings on a pioneering work of Erich Lehmann and its rediscoveries on some families of distributions
Bibliographic record
Abstract
The paper of Erich Lehmann (Lehmann Citation1953) is renowned for its ground-breaking contribution to rank tests within the area of nonparametric statistics. This work, in all likelihood, is known to every statistician. But, what is likely not known to many are some of the novel concepts and models that this paper succinctly introduced to the area of distribution theory. This, unfortunately, has led to some of these being rediscovered in the literature and then being referred to under different names. The purpose of this note is, therefore, two-fold: first to explain the key models that are contained in the mentioned work of Erich Lehmann, and second to point out how some of the known models discussed in the distribution theory and stochastic modeling literature are indeed present either explicitly or implicitly in the paper of Lehmann.
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 | 0.006 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.004 | 0.011 |
| Scholarly communication | 0.005 | 0.016 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.003 | 0.011 |
| Insufficient payload (model declined to judge) | 0.009 | 0.005 |
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".