Paper Citation Record · LEDGER

Estimation of the number of principal components in high-dimensional multivariate extremes

As of 19 August 2026, this Paper Citation Record lists 41 of 41 outbound references and 0 inbound Pith citation observations for arXiv:2505.22437.

A citation records a reference. It does not transfer a finding from one paper to another.

pith.paper-citation-record.v1
2505.22437 v1

Coverage vector

measured 41 of 41 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T13:16:10.353596Z

measured 41 of 41 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-18T06:34:40.430872+00:00

measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

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measured 0 of 1 external citation measurements

A source-named dated measurement, never combined with another source.

Source: cited_works

Reference resolution

41 of 41 outbound references displayed

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External citation measurements

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Outbound references

Observation 1e81ecee-9293-4124-8c5c-39823075a685 · outbound

This paper cites an unresolved cited work.

Estimation of the number of principal components in high-dimensional multivariate extremes Unresolved cited work

Reference 1

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:16:19.581877Z

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Estimation of the number of principal components in high-dimensional multivariate extremes Unresolved cited work

Reference 2

Resolution: unresolved; raw_fallback, observed 2026-08-07T13:16:19.377552Z

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Observation 54cef6f6-2914-4f72-8533-2ae183acc80b · outbound

This paper cites Kernel PCA for multivariate extremes.

Estimation of the number of principal components in high-dimensional multivariate extremes Kernel PCA for multivariate extremes

Reference 3

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Estimation of the number of principal components in high-dimensional multivariate extremes Unresolved cited work

Reference 4

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This paper cites Strong consistency of the AIC, BIC, $C_p$ and KOO methods in high-dimensional multivariate linear regression.

Estimation of the number of principal components in high-dimensional multivariate extremes Strong consistency of the AIC, BIC, $C_p$ and KOO methods in high-dimensional multivariate linear regression

Reference 5

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This paper cites and SILVERSTEIN , J.

Estimation of the number of principal components in high-dimensional multivariate extremes and SILVERSTEIN , J

Reference 6

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This paper cites and YAO, J.

Estimation of the number of principal components in high-dimensional multivariate extremes and YAO, J

Reference 7

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Estimation of the number of principal components in high-dimensional multivariate extremes Unresolved cited work

Reference 8

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This paper cites Information criteria for the number of directions of extremes in high-dimensional data.

Estimation of the number of principal components in high-dimensional multivariate extremes Information criteria for the number of directions of extremes in high-dimensional data

Reference 9

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Estimation of the number of principal components in high-dimensional multivariate extremes Unresolved cited work

Reference 10

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Observation 7e0bf145-2a07-48a0-9cde-82e4aca70c58 · outbound

This paper cites and S ABOURIN , A.

Estimation of the number of principal components in high-dimensional multivariate extremes and S ABOURIN , A

Reference 11

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Estimation of the number of principal components in high-dimensional multivariate extremes and T HIBAUD , E

Reference 12

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Estimation of the number of principal components in high-dimensional multivariate extremes and ROMAIN , Y

Reference 13

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Estimation of the number of principal components in high-dimensional multivariate extremes Unresolved cited work

Reference 14

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Estimation of the number of principal components in high-dimensional multivariate extremes and F ERREIRA , A

Reference 15

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Observation 171bf296-1ea2-4bdc-869c-d61732aff48c · outbound

This paper cites Asymptotic Behavior of Principal Component Projections for Multivariate Extremes.

Estimation of the number of principal components in high-dimensional multivariate extremes Asymptotic Behavior of Principal Component Projections for Multivariate Extremes

Reference 16

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This paper cites and S ABOURIN , A.

Estimation of the number of principal components in high-dimensional multivariate extremes and S ABOURIN , A

Reference 17

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Observation e6196b03-84ba-432f-9680-01a04c40830b · outbound

This paper cites Daily station observations precipitation height in mm for Germany, version v21.3, last accessed: May 03, 2023.

Estimation of the number of principal components in high-dimensional multivariate extremes Daily station observations precipitation height in mm for Germany, version v21.3, last accessed: May 03, 2023

Reference 18

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This paper cites and P E ˘CARI ´C, J.

Estimation of the number of principal components in high-dimensional multivariate extremes and P E ˘CARI ´C, J

Reference 19

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Estimation of the number of principal components in high-dimensional multivariate extremes and I VANOVS , J

Reference 20

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Estimation of the number of principal components in high-dimensional multivariate extremes Unresolved cited work

Reference 21

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Estimation of the number of principal components in high-dimensional multivariate extremes and SAKURAI , T

Reference 22

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Estimation of the number of principal components in high-dimensional multivariate extremes V., M CKEAN , J

Reference 23

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Estimation of the number of principal components in high-dimensional multivariate extremes Unresolved cited work

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This paper cites On eigenvalues of sample covariance matrices based on high dimensional compositional data.

Estimation of the number of principal components in high-dimensional multivariate extremes On eigenvalues of sample covariance matrices based on high dimensional compositional data

Reference 25

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Estimation of the number of principal components in high-dimensional multivariate extremes Unresolved cited work

Reference 26

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This paper cites Notes on asymptotics of sample eigenstructure for spiked covariance models with non-Gaussian data.

Estimation of the number of principal components in high-dimensional multivariate extremes Notes on asymptotics of sample eigenstructure for spiked covariance models with non-Gaussian data

Reference 27

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Estimation of the number of principal components in high-dimensional multivariate extremes and R ESNICK , S

Reference 28

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Estimation of the number of principal components in high-dimensional multivariate extremes Unresolved cited work

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Estimation of the number of principal components in high-dimensional multivariate extremes and W INTENBERGER , O

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Estimation of the number of principal components in high-dimensional multivariate extremes Unresolved cited work

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Estimation of the number of principal components in high-dimensional multivariate extremes Unresolved cited work

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Estimation of the number of principal components in high-dimensional multivariate extremes Unresolved cited work

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Estimation of the number of principal components in high-dimensional multivariate extremes Unresolved cited work

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Estimation of the number of principal components in high-dimensional multivariate extremes and C OOLEY , D

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Estimation of the number of principal components in high-dimensional multivariate extremes Unresolved cited work

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Estimation of the number of principal components in high-dimensional multivariate extremes Unresolved cited work

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Estimation of the number of principal components in high-dimensional multivariate extremes Unresolved cited work

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Estimation of the number of principal components in high-dimensional multivariate extremes Unresolved cited work

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Estimation of the number of principal components in high-dimensional multivariate extremes Unresolved cited work

Reference 40

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Estimation of the number of principal components in high-dimensional multivariate extremes Q., B AI, Z

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