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Record W2343970457 · doi:10.1080/03610926.2014.901365

Complete moment convergence of weighted sums for arrays of negatively dependent random variables and its applications

2015· article· en· W2343970457 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

VenueCommunication in Statistics- Theory and Methods · 2015
Typearticle
Languageen
FieldDecision Sciences
TopicProbability and Risk Models
Canadian institutionsUniversity of Regina
FundersNatural Science Foundation of Anhui ProvinceTongling UniversityNational Science Foundation
KeywordsConvergence (economics)Moment (physics)Pairwise comparisonMathematicsRandom variablePairwise independenceVariablesApplied mathematicsProofs of convergence of random variablesStatisticsConvergence of random variablesSum of normally distributed random variablesPhysics

Abstract

fetched live from OpenAlex

The authors study the complete moment convergence of weighted sums for arrays of rowwise negatively dependent random variables. The obtained results improve the corresponding results of Baek and Park (2010). Convergence of weighted sums for arrays of negatively dependent random variables and its applications. As an application, the authors obtain the complete moment convergence of linear processes based on pairwise negatively dependent random variables. In addition, the authors point out a gap of the proof in Baek and Park (2010) and raise an open problem.

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)0.020
metaresearch head score (Gemma)0.005
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.407
Threshold uncertainty score0.683

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0200.005
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 science0.0010.000
Research integrity0.0000.000
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 head0.227
GPT teacher head0.480
Teacher spread0.252 · 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