Tests for non-correlation of two multivariate time series: a nonparametric approach
Bibliographic record
Abstract
Most of the recent results on tests for non-correlation between two time series are based on the residual serial cross-correlation matrices resulting from appropriate modelling of the two series.However in the stationary case, test procedures can be defined from the serial cross-correlation of the original series, avoiding therefore the modelling stage.This paper aims at describing two such tests that take into account a finite number of lagged cross-correlations.The first one that is essentially valid for Gaussian time series makes use of a procedure for estimating the covariance structure of serial correlations described in Melard, Paesmans and Roy (1991).The second one that is valid for a general class of mixing processes is based on the property that the cross-covariance at a given lag between two stationary processes is in fact the mean of the product of the two processes, the second one being lagged appropriately.For both approaches, the asymptotic distributions of the test statistics are derived under the null hypothesis of non-correlation between the two series.The level and power of the proposed tests are studied by simulation in finite samples and an example is presented.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.025 | 0.143 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.006 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".