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Paper Citation Record · LEDGER

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses

As of 17 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 1 inbound Pith citation observation for arXiv:2504.15510.

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

pith.paper-citation-record.v1
2504.15510 v3

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T11:31:17.387378Z

measured 23 of 23 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-17T06:30:58.91139+00:00

measured 1 of 1 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T11:15:04.329014Z

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

22 of 22 outbound references displayed

  • verified exact0
  • verified fuzzy18
  • unresolved3
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch1

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation b8accb99-515a-45a0-86e7-b80be7999ae5 · outbound

This paper cites Isotropic local laws for sample covariance and generalized Wigner matrices.

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses Isotropic local laws for sample covariance and generalized Wigner matrices

Reference 1

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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:31:17.291417Z digest=sha256:c7d94399e23d3f0885ec2ed7da6a1c9e6db27368e9586345ab592f1ae57b05f0

Observation 7ffa41f6-6bd6-4fd1-acb2-cf9f83dcbc83 · outbound

This paper cites Two-Sample Tests for High Dimensional Means with Thresholding and Data Transformation.

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses Two-Sample Tests for High Dimensional Means with Thresholding and Data Transformation

Reference 2

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local_arxiv, observed 2026-08-16T11:31:17.446444Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:31:17.296586Z digest=sha256:735c0d245b613498d9c85b7e76702d65ed211db5b13365f7296c24ddb92c381a

Observation 5f0fcd88-8bdb-4112-8162-6e5c9e7a528b · outbound

This paper cites (3) There exists sufficiently small constants C ą 0 and C1ą 0 such that for all zPtE` iη : |E´ Θ1|ď C, 0ăηăC´1u inf τPSGλ |τ`qpzq|ě C1, whereSGλ is the support of Gλ.

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses (3) There exists sufficiently small constants C ą 0 and C1ą 0 such that for all zPtE` iη : |E´ Θ1|ď C, 0ăηăC´1u inf τPSGλ |τ`qpzq|ě C1, whereSGλ is the support of Gλ

Reference 6

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:31:17.316629Z digest=sha256:f083adcd074e82ee1ba0db2e10becf6a368f7072626ec2a963397fb1dd8ea6db

Observation 9f67c5a3-ae6a-42b8-838f-8d4cdde95644 · outbound

This paper cites Let zPD and suppose that |˜z´z|ď δpzq, where ˜z“´ 1{upzq` γ2ϕp´upzqq.

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses Let zPD and suppose that |˜z´z|ď δpzq, where ˜z“´ 1{upzq` γ2ϕp´upzqq

Reference 7

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:31:17.322010Z digest=sha256:529ece0c6e1300add40d6f7ba001cac0e4cf823710c3c60ffabebbf63c39d3f8

Observation 8fe15588-6bbc-47e1-bb9d-c399e900830d · outbound

This paper cites Let zPQ and suppose that |˜z´z|ď δpzq, where ˜z“vpzq` „ 1`γ2 ż τdF Σ8pτq ´τvpzq` λ ȷ´1.

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses Let zPQ and suppose that |˜z´z|ď δpzq, where ˜z“vpzq` „ 1`γ2 ż τdF Σ8pτq ´τvpzq` λ ȷ´1

Reference 8

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source=pdf_text observed=2026-08-16T11:31:17.326567Z digest=sha256:1ebaf1244e898e38665862b26468e4a0b010a69f002038e2e6a049d8c1c25375

Observation e9ffe472-2516-417c-a29f-8996929807ea · outbound

This paper cites (ii) The averaged local law in Qppq holds as ˆϕppzq´ ϕppzq“ Oă ˆ 1 n2η ˙ , where ˆϕppzq“ p´1trRpzq.

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses (ii) The averaged local law in Qppq holds as ˆϕppzq´ ϕppzq“ Oă ˆ 1 n2η ˙ , where ˆϕppzq“ p´1trRpzq

Reference 9

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source=pdf_text observed=2026-08-16T11:31:17.331378Z digest=sha256:6b2f3b5fdf97f28877893ab384620823d9f5d509b7e854ef2f0a9e1f9d3de99e

Observation 2a69cd31-6ff5-4f46-853e-150bd689c340 · outbound

This paper cites We have }J}ď C η3, }BzJ}ď C η6.

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses We have }J}ď C η3, }BzJ}ď C η6

Reference 10

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source=pdf_text observed=2026-08-16T11:31:17.336185Z digest=sha256:259b08f8f70674e7833cdf86ccc9787fb39aa93f41a183e48d3374c5d0334008

Observation ebd0f181-3941-457c-a9ba-d6219413812a · outbound

This paper cites an unresolved cited work.

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses Unresolved cited work

Reference 11

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source=pdf_text observed=2026-08-16T11:31:17.340686Z digest=sha256:b31fd6b6efc699874b93873de4b35139c939e650e3b9daa74ef935bbcca2f357

Observation ebcb2337-7e4d-4aa5-b92e-fc4a6fac882f · outbound

This paper cites Let us first estimate Λ o.

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses Let us first estimate Λ o

Reference 12

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source=pdf_text observed=2026-08-16T11:31:17.344571Z digest=sha256:d6dfc9f1edd3eda22a09700a218a81c01c2880b440cba3945a2b4e7fac22c1bd

Observation b8f9fb46-9f96-4d89-adee-e2aef21b7f93 · outbound

This paper cites Applying Lemma S.4.6 or Corollary S.4.3, when ηě 1, |ˆϕp´ϕp|ď Cp|δ1|`| δ2|q ă ΨB ăn´1{2 2.

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses Applying Lemma S.4.6 or Corollary S.4.3, when ηě 1, |ˆϕp´ϕp|ď Cp|δ1|`| δ2|q ă ΨB ăn´1{2 2

Reference 13

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:31:17.348610Z digest=sha256:4d0e232867e98791760f34db6db01435e29d4fd763487c0834d570d84c16a7b6

Observation 4f600a91-8bb3-4b8e-9972-2529789cac6b · outbound

This paper cites Then on QppqYQppq away we have 1 p pÿ u“1 1 pλ{σu´z´ 1 n2 řp`n2 i“p`1 Jiiq2p1´ Euq 1 Juu “Oă ` Υ2˘ and 1 n2 p`n2ÿ i“p`1 p1´ Eiq 1 Jii “Oă ` Υ2˘.

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses Then on QppqYQppq away we have 1 p pÿ u“1 1 pλ{σu´z´ 1 n2 řp`n2 i“p`1 Jiiq2p1´ Euq 1 Juu “Oă ` Υ2˘ and 1 n2 p`n2ÿ i“p`1 p1´ Eiq 1 Jii “Oă ` Υ2˘

Reference 14

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source=pdf_text observed=2026-08-16T11:31:17.352497Z digest=sha256:eb75f44ed45f1d83475744b0f9d66f6d29a7e33474200e96b67b753763e6b05e

Observation 850edf40-f6e9-449a-9464-caa1e20c1017 · outbound

This paper cites Therefore, to show ˇˇˇˇˇ 1 p pÿ j“1 fpℓjpGλqq´ ż fpτqdGλpτq ˇˇˇˇˇ ă 1 n2 , it suffices to show ¿ ˆR |fpzq| ˇˇˇˆϕppzq´ ϕppzq ˇˇˇdz ă 1 n2.

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses Therefore, to show ˇˇˇˇˇ 1 p pÿ j“1 fpℓjpGλqq´ ż fpτqdGλpτq ˇˇˇˇˇ ă 1 n2 , it suffices to show ¿ ˆR |fpzq| ˇˇˇˆϕppzq´ ϕppzq ˇˇˇdz ă 1 n2

Reference 15

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source=pdf_text observed=2026-08-16T11:31:17.356527Z digest=sha256:8bd1ca72ea714822ecf17a293bb064a2ccdee9a6eacc6e66b1e54fcdf0995b60

Observation 1ef244ac-db55-4e06-9413-2cfd6ca01279 · outbound

This paper cites local laws.

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses local laws

Reference 16

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source=pdf_text observed=2026-08-16T11:31:17.360865Z digest=sha256:c9ed2771571bfba6e75f6aa07a3f964b0020cfff0d215dc04d05223748e3f748

Observation ebf4bad1-79d4-4be6-8149-a3606d1a58b8 · outbound

This paper cites Let ˆΘp1“ ˆfp´ ˆβpq and ˆΦpzq“ d ℑˆqppzq n1η ` 1 n1η.

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses Let ˆΘp1“ ˆfp´ ˆβpq and ˆΦpzq“ d ℑˆqppzq n1η ` 1 n1η

Reference 17

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source=pdf_text observed=2026-08-16T11:31:17.364887Z digest=sha256:da25df1b85757a15f9db75e5200f931446bc91539eabbcfa7825103c59fdd03c

Observation 04257bee-d96f-4f8f-8644-8ffaaacd0b84 · outbound

This paper cites Therefore, ´ˆqppzq is away from the support of Gλ.

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses Therefore, ´ˆqppzq is away from the support of Gλ

Reference 18

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source=pdf_text observed=2026-08-16T11:31:17.368971Z digest=sha256:3d702ac69387eaa15ee9fcd79bcf91e1b7b88adb0a0afbac2fe5fbd86a5f6b35

Observation 245fbb02-55b7-4710-8dde-03c0f388bb33 · outbound

This paper cites an unresolved cited work.

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses Unresolved cited work

Reference 19

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source=pdf_text observed=2026-08-16T11:31:17.373609Z digest=sha256:1461254e275a1f037b48f15ad2be5a702240e2900ca70a0f52eefef76ccd9510

Observation 52f07a72-df28-4b62-84d0-46fb168c377c · outbound

This paper cites 1 pνTD1{2pW2`λIpq´1D1{2ν`oPp1q. Using Lemma 2.7 of Bai and Silverstein (1998), conditional on W2, 1 pνTD1{2pW2`λIpq´1D1{2ν“ 1 ptr.

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses 1 pνTD1{2pW2`λIpq´1D1{2ν`oPp1q. Using Lemma 2.7 of Bai and Silverstein (1998), conditional on W2, 1 pνTD1{2pW2`λIpq´1D1{2ν“ 1 ptr

Reference 20

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source=pdf_text observed=2026-08-16T11:31:17.378364Z digest=sha256:f6175dce9a111fbf8cdfcf33d32e4ea6e3c021030f35efbdc4e73d4afbcfe620

Observation 68f4c7e5-6870-4b31-a1af-22cfb74939a5 · outbound

This paper cites Therefore, s1pxq“ C pρ´xq2 `C ż τąρ dGλpτq τ´x Ñ8, as xÒρ.

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses Therefore, s1pxq“ C pρ´xq2 `C ż τąρ dGλpτq τ´x Ñ8, as xÒρ

Reference 21

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source=pdf_text observed=2026-08-16T11:31:17.382924Z digest=sha256:ff9fb8c4448b5aa584335ecb616de218eb77c4100354a0b205503247f647258f

Observation bad5e9c7-0701-4e4c-894c-212e964dbec7 · outbound

This paper cites Using Theorem 5.1 of Li (2024), there exists constants C1, C2 and ϵ such that C1 ?x´ρďfGλpxqď C2 ?x´ρ, x Ppρ,ρ `ϵq where fGλ is the density function of Gλ.

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses Using Theorem 5.1 of Li (2024), there exists constants C1, C2 and ϵ such that C1 ?x´ρďfGλpxqď C2 ?x´ρ, x Ppρ,ρ `ϵq where fGλ is the density function of Gλ

Reference 22

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source=pdf_text observed=2026-08-16T11:31:17.387378Z digest=sha256:5cc2fdc9810872d498799100fbecedf5c27c08a7e9f5d27972d2c1594415106c

Observation 7791cb86-bae8-46c4-884d-beac592613f1 · outbound

This paper cites Pillai, N.

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses Pillai, N

Reference 197

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source=pdf_text observed=2026-08-16T11:31:17.306873Z digest=sha256:2104aad914a01bb513fbf8c3cc0e537c5ea2c83bfd0cc5e02b4a1b93f4b69482

Observation d5137852-438b-4917-9d4b-4b8ead605d0f · outbound

This paper cites Ridge-regularized Largest Root Test for High-Dimensional General Linear Hypotheses.

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses Ridge-regularized Largest Root Test for High-Dimensional General Linear Hypotheses

Reference 1001

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raw_fallback, observed 2026-08-16T11:31:17.714233Z

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No event found in the named queried sources as of 2026-08-17T06:30:58.91139+00:00.

source=pdf_text observed=2026-08-16T11:31:17.312019Z digest=sha256:a6a7bb1a1abb0182686efc598e839a1ecf24fe7d1c554c9f8a4ee39ab57d08e8

Observation 0968d010-c3d0-4921-bc51-8ac28e588165 · outbound

This paper cites Geometric sensitivity of random matrix results: consequences for shrinkage estimators of covariance and related statistical methods.

Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses Geometric sensitivity of random matrix results: consequences for shrinkage estimators of covariance and related statistical methods

Reference 2006

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=pdf_text observed=2026-08-16T11:31:17.301713Z digest=sha256:56638cdcb8c64982251a2a61f95d8a29957f00f84f401d46c63217aa93fd5704

Pith citing papers

Observation c57e03ce-7d57-4395-83f2-86116cc2d6e1 · inbound

Adaptable Regularized CCA Tests for Independence of High-Dimensional Random Vectors cites this paper.

Adaptable Regularized CCA Tests for Independence of High-Dimensional Random Vectors Ridge-Regularized Largest Root Test For High-Dimensional General Linear Hypotheses

Reference 144

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T11:15:04.329014Z digest=sha256:881a143334ea4e1c062d4e837f2c23e5c9658e005f90290642d36b1389b843ad