Evidence map›Paper›PMID 36685139›Full record

ArticleBiometrika2022

Significance testing for canonical correlation analysis in high dimensions.

Ian W McKeague, Xin Zhang

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Biometrika, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Article
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

2 authors.

Ian W McKeagueDepartment of Biostatistics, Columbia University, Room R639, 722 West 168th Street, New York, NY 10032, USA.
Xin ZhangDepartment of Statistics, Florida State University, 214 OSB, 117 N. Woodward Ave., Tallahassee, FL 32306, USA.

Funding

Multimodal Integrative Dimension Reduction and Statistical Modeling with Applications to Temporomandibular Joint (TMJ) Morphometry and BiomechanicsR03DE030509 · NIDCR · FLORIDA STATE UNIVERSITY · PI SLATE, ELIZABETH H., ZHANG, XIN · 2021 to 2022
$309k
NIDCR NIH HHS R03 DE030509
6 · The paper itself

Abstract

We consider the problem of testing for the presence of linear relationships between large sets of random variables based on a post-selection inference approach to canonical correlation analysis. The challenge is to adjust for the selection of subsets of variables having linear combinations with maximal sample correlation. To this end, we construct a stabilized one-step estimator of the euclidean-norm of the canonical correlations maximized over subsets of variables of pre-specified cardinality. This estimator is shown to be consistent for its target parameter and asymptotically normal, provided the dimensions of the variables do not grow too quickly with sample size. We also develop a greedy search algorithm to accurately compute the estimator, leading to a computationally tractable omnibus test for the global null hypothesis that there are no linear relationships between any subsets of variables having the pre-specified cardinality. We further develop a confidence interval that takes the variable selection into account.

Indexed as

Efficient one-step estimatorGreedy search algorithmLarge-scale testingPost-selection inference

Identifiers

PMID36685139
PMCPMC9857302

What OpenQuestion holds

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Registered trials

None linked

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.