Evidence map›Paper›PMID 38034126›Full record

ArticleRoyal Society open science2023

Kernel-based joint independence tests for multivariate stationary and non-stationary time series.

Zhaolu Liu, Robert L Peach, Felix Laumann, Sara Vallejo Mengod, Mauricio Barahona

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Royal Society open science, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

5 authors.

Zhaolu LiuDepartment of Mathematics, Imperial College London, London SW7 2AZ, UK.ORCID 0000-0002-8721-7506
Robert L PeachDepartment of Brain Sciences, Imperial College London, London W12 0NN, UK.
Felix LaumannDepartment of Mathematics, Imperial College London, London SW7 2AZ, UK.
Sara Vallejo MengodDepartment of Mathematics, Imperial College London, London SW7 2AZ, UK.
Mauricio BarahonaDepartment of Mathematics, Imperial College London, London SW7 2AZ, UK.ORCID 0000-0002-1089-5675

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Multivariate time-series data that capture the temporal evolution of interconnected systems are ubiquitous in diverse areas. Understanding the complex relationships and potential dependencies among co-observed variables is crucial for the accurate statistical modelling and analysis of such systems. Here, we introduce kernel-based statistical tests of joint independence in multivariate time series by extending the

Indexed as

higher-order interactionjoint independence testkernel methodmultivariate time-series analysis

Identifiers

PMID38034126
PMCPMC10685129

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.