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Record W2090513398 · doi:10.5539/ijsp.v3n2p1

Estimation of a Spearman-Type Multivariate Measure of Local Dependence

2014· article· en· W2090513398 on OpenAlexvenueno aff
Sumaia Abdel Latif, Pedro A. Morettin

Bibliographic record

VenueInternational Journal of Statistics and Probability · 2014
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicFinancial Risk and Volatility Modeling
Canadian institutionsnot available
Fundersnot available
KeywordsMultivariate statisticsEstimatorMathematicsMeasure (data warehouse)Nonparametric statisticsStatisticsEconometricsMultivariate analysisPopulationSample (material)Series (stratigraphy)Computer scienceData mining

Abstract

fetched live from OpenAlex

A multivariate measure of local dependence written in terms of copulas is proposed, which if integrated, coincides with a population version of a multivariate global measure of Spearman's rho. We propose nonparametric estimators of this measure for independent sample data and also for time series data. Some properties of the estimators are derived. Simulations with different copulas and sample sizes were performed to assess the theoretical findings. Empirical applications are given for selected economic indexes of $166$ countries and for the returns of the DAX, CAC40 and FTSE indexes.

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 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)0.001
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: Empirical · Consensus signal: none
Teacher disagreement score0.603
Threshold uncertainty score0.252

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.035
GPT teacher head0.269
Teacher spread0.234 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2014
Admission routes1
Has abstractyes

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