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Record W4220754119 · doi:10.1016/j.spl.2022.109448

A Berry–Esseen bound for vector-valued martingales

2022· article· en· W4220754119 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

VenueStatistics & Probability Letters · 2022
Typearticle
Languageen
FieldMathematics
TopicMathematical Approximation and Integration
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMathematicsConditional varianceConditional expectationConditional probability distributionMartingale difference sequenceBounded functionMartingale (probability theory)CombinatoricsUpper and lower boundsMathematical analysisApplied mathematicsStatisticsAutoregressive conditional heteroskedasticityEconometrics

Abstract

fetched live from OpenAlex

This note provides a conditional Berry–Esseen bound for the sum of a martingale difference sequence {Xi}i=1n in Rd, d≥1, adapted to a filtration {Fi}i=1n. We approximate the conditional distribution of S=∑i=1nXi given a sub-σ-field F0⊂F1 by that of a mean zero normal random vector having the same conditional variance given F0 as the vector S. Assuming that the conditional variances E[XiXi⊤∣Fi−1], i≥1, are F0-measurable and non-singular, and the third conditional moments of ‖Xi‖, i≥1, given F0 are uniformly bounded, we present a simple bound on the conditional Kolmogorov distance between S and its approximation given F0 which is of order Oa.s.([ln(ed)]5/4n−1/4).

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)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
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 score0.066
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.067
GPT teacher head0.325
Teacher spread0.258 · 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