Evidence map›Paper›PMID 34772810›Full record

ArticleProceedings of the National Academy of Sciences of the United States of America2021

Taylor's law of fluctuation scaling for semivariances and higher moments of heavy-tailed data.

Mark Brown, Joel E Cohen, Chuan-Fa Tang, Sheung Chi Phillip Yam

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Proceedings of the National Academy of Sciences of the United States of America, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

0numbers the graph read from it
0cells of the map it votes in
2citing 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

2 citing papers in PubMed.

  1. Article
  2. Taylor's law and heavy-tailed distributions.Proceedings of the National Academy of Sciences of the United States of America · 2021
    Article
4 · The record

Corrections and comments

5 · Who and what money

Authors and funding

4 authors.

Mark BrownDepartment of Statistics, Columbia University, New York, NY 10027; mb2484@columbia.edu cohen@rockefeller.edu chuan-fa.tang@utdallas.edu scpyam@sta.cuhk.edu.hk.
Joel E CohenLaboratory of Populations, The Rockefeller University, New York, NY 10065-6399; mb2484@columbia.edu cohen@rockefeller.edu chuan-fa.tang@utdallas.edu scpyam@sta.cuhk.edu.hk.ORCID 0000-0002-9746-6725
Chuan-Fa TangDepartment of Mathematical Sciences, University of Texas at Dallas, Richardson, TX 75080; mb2484@columbia.edu cohen@rockefeller.edu chuan-fa.tang@utdallas.edu scpyam@sta.cuhk.edu.hk.
Sheung Chi Phillip YamDepartment of Statistics, Chinese University of Hong Kong, Hong Kong mb2484@columbia.edu cohen@rockefeller.edu chuan-fa.tang@utdallas.edu scpyam@sta.cuhk.edu.hk.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

We generalize Taylor's law for the variance of light-tailed distributions to many sample statistics of heavy-tailed distributions with tail index

Indexed as

Paretopower lawsemivariancestable lawTaylor’s law

Identifiers

PMID34772810
PMCPMC8609639

What OpenQuestion holds

Textmetadata
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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.