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Record W3093996040 · doi:10.1214/21-aihp1239

On mean estimation for heteroscedastic random variables

2023· article· fr· W3093996040 on OpenAlex
Luc Devroye, Silvio Lattanzi, Gábor Lugosi, Nikita Zhivotovskiy

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

VenueAnnales de l Institut Henri Poincaré Probabilités et Statistiques · 2023
Typearticle
Languagefr
FieldComputer Science
TopicBayesian Methods and Mixture Models
Canadian institutionsMcGill University
Fundersnot available
KeywordsMathematicsHeteroscedasticityCombinatoricsStatistics

Abstract

fetched live from OpenAlex

Nous étudions le problème de l’estimation de la moyenne commune μ de n variables aléatoires symétriques indépendantes avec des écarts types différents et inconnus σ1≤σ2≤⋯≤σn. Nous montrons que, sous faibles hypothèses de régularité sur la distribution, il existe un estimateur adaptatif μˆ invariant par rapport aux permutations des éléments de l’échantillon qui satisfait à facteurs logarithmiques près et avec une grande probabilité |μˆ−μ|≲min{σm∗,n ∑ i=nnσi−1}, où l’indice m∗≲n satisfait m∗≈ σm∗∑ i=m∗nσi−1.

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)0.005
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.389
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0010.001
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.065
GPT teacher head0.362
Teacher spread0.297 · 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