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Gumbel’s Identity, Binomial Moments, and Bonferroni Sums

2012· article· fr· W1871331304 on OpenAlex

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.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Statistical Review · 2012
Typearticle
Languagefr
FieldMathematics
TopicMathematical Inequalities and Applications
Canadian institutionsMcMaster University
Fundersnot available
KeywordsGumbel distributionMathematicsCombinatoricsBinomial (polynomial)Bonferroni correctionHumanitiesPhilosophyStatisticsExtreme value theory

Abstract

fetched live from OpenAlex

Résumé L'identité de Gumbel établit l'égalité de la somme de Bonferroni S k,n , k = 0, 1, 2, … , n et du moment binomial d'ordre k de la variable qui compte, dans un ensemble arbitraire de n événements, le nombre M n d'événements se réalisant. Nous présentons un traitement unifié de bornes bien connues obtenues dans ce contexte par Bonferroni, Galambos‐Rényi, Dawson‐Sankoff et Chung‐Erdös, ainsi que de quelques bornes moins connues établies par Fréchet et Gumbel. Toutes font intervenir des sommes de Bonferroni. Notre démarche consiste à montrer que ces bornes apparaissent dans un cadre plus général comme les moments binomiaux d'une variable aléatoire à valeurs entières particulière. L'application de l'identité de Gumbel fournit alors la forme usuelle en termes de sommes de Bonferroni. Notre approche simplifie les preuves existantes, et permet d'étendre les résultats de Fréchet et Gumbel au cas de la probabilité pour qu'au moins t , 1 ≤ t ≤ n des n événements considérés se réalisent. Une dernière conséquence de notre approche est l'amélioration d'une borne de Petrov qui elle‐même est la généralisation de la borne de Chung et Erdös.

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 imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Insufficient payload (model declined to judge)
Consensus categoriesInsufficient payload (model declined to judge)
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: none
Teacher disagreement score0.553
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0220.002

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.

Opus teacher head0.123
GPT teacher head0.448
Teacher spread0.325 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it