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Paper Citation Record · LEDGER

Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition

As of 19 August 2026, this Paper Citation Record lists 50 of 50 outbound references and 0 inbound Pith citation observations for arXiv:2506.06919.

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

pith.paper-citation-record.v1
2506.06919 v1

Coverage vector

measured 50 of 50 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-07T05:55:33.577987Z

measured 50 of 50 standing notices

One-hop event checks from named stored sources.

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measured 0 of 0 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links

measured 0 of 1 external citation measurements

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

Source: cited_works

Reference resolution

50 of 50 outbound references displayed

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  • verified fuzzy11
  • unresolved35
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External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 36a43c24-ea4e-448a-8ca7-9f7ff06b4eb2 · outbound

This paper cites an unresolved cited work.

Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition Unresolved cited work

Reference 1

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Observation db263117-4841-44c3-b0fc-163176310dee · outbound

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Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition Unresolved cited work

Reference 2

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This paper cites and Wang, P.

Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition and Wang, P

Reference 3

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This paper cites Statistical inference for large-dimensional tensor factor model by iterative projections.

Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition Statistical inference for large-dimensional tensor factor model by iterative projections

Reference 4

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Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition and Michailidis, G

Reference 5

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Reference 6

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Observation 07215e20-24dc-4352-b133-f6b5f9add766 · outbound

This paper cites Large Bayesian Tensor VARs with Stochastic Volatility.

Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition Large Bayesian Tensor VARs with Stochastic Volatility

Reference 7

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Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition Unresolved cited work

Reference 8

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Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition Unresolved cited work

Reference 9

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Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition Unresolved cited work

Reference 10

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Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition Unresolved cited work

Reference 11

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Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition and Lam, C

Reference 12

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Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition Unresolved cited work

Reference 13

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Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition and Marcellino, M

Reference 16

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Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition and Zhang, A

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Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition P., Reinsel, G

Reference 35

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Source-reported events for the cited work

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Observation 842439ce-7f28-42b2-b9a4-6c58bc689b0d · outbound

This paper cites an unresolved cited work.

Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition Unresolved cited work

Reference 38

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Source-reported events for the cited work

Unavailable: canonical work link unavailable.

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Observation 933927e7-39ec-4d88-8150-9615802684c6 · outbound

This paper cites an unresolved cited work.

Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition Unresolved cited work

Reference 39

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition and Ng, M

Reference 40

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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This paper cites an unresolved cited work.

Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition Unresolved cited work

Reference 41

Resolution
unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation ba040519-67b9-4eba-98cd-a3959bbf80c2 · outbound

This paper cites an unresolved cited work.

Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition Unresolved cited work

Reference 42

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 13d8cab3-290a-4858-942d-0d882e09cf5a · outbound

This paper cites and Teboulle, M.

Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition and Teboulle, M

Reference 43

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation b2ba8002-f9b0-452b-93ba-5dd4e781415a · outbound

This paper cites M., and Chen, Y.

Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition M., and Chen, Y

Reference 44

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation ba740275-816c-4e82-869d-6bc04c3c3bad · outbound

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Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition Unresolved cited work

Reference 45

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 9c48060b-ea9b-41ee-8a2b-403bcd82afff · outbound

This paper cites SARMA: Scalable Low-Rank High-Dimensional Autoregressive Moving Averages via Tensor Decomposition.

Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition SARMA: Scalable Low-Rank High-Dimensional Autoregressive Moving Averages via Tensor Decomposition

Reference 46

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation e42a85a1-d775-415d-bf19-74015adfc003 · outbound

This paper cites J., and Yu, B.

Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition J., and Yu, B

Reference 47

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation f02607a4-81ed-4f86-9bb0-58f0b5df7926 · outbound

This paper cites W., Lu, J., Liu, H., and Cheng, G.

Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition W., Lu, J., Liu, H., and Cheng, G

Reference 48

Resolution
verified fuzzy
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 9bf82eba-df42-48f4-b823-6440825d9e3e · outbound

This paper cites an unresolved cited work.

Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition Unresolved cited work

Reference 49

Resolution
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Observation 630b6c6d-e9cb-40a7-ae2b-32aad4231113 · outbound

This paper cites an unresolved cited work.

Tensor Stochastic Regression for High-dimensional Time Series via CP Decomposition Unresolved cited work

Reference 50

Resolution
unresolved
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Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.

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Pith citing papers

No inbound Pith citation observations are available.