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Paper Citation Record · LEDGER

Inference for Change Points in High Dimensional Data via Self-Normalization

As of 11 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1905.08446.

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

pith.paper-citation-record.v1
1905.08446 v2

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 2 of 2 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-11T06:34:44.6726+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-07-14T05:58:32.336515Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-07-02T20:17:21.624779Z

Reference resolution

0 of 0 outbound references displayed

  • verified exact0
  • verified fuzzy0
  • unresolved0
  • parse uncertain0
  • malformed identifier0
  • metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 6420a2d6-f76a-4d89-9b3d-266bea2402a2 · inbound

High Dimensional Change Point Models for Two-Directional Data cites this paper.

High Dimensional Change Point Models for Two-Directional Data Inference for Change Points in High Dimensional Data via Self-Normalization

Reference 74

Resolution
verified exact
arxiv_id, observed 2026-07-02T20:17:21.626142Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.

source=arxiv_source observed=2026-06-27T20:46:26.231549Z digest=sha256:44dda31f26f14c56ccc857307ab55101b58991bcb912f8ac111eeff0b2cef893

Observation 779afb9a-064c-4636-a859-00d7176abaf8 · inbound

A General U-Statistic Framework for High-Dimensional Multiple Change-Point Analysis cites this paper.

A General U-Statistic Framework for High-Dimensional Multiple Change-Point Analysis Inference for Change Points in High Dimensional Data via Self-Normalization

Reference 95

Resolution
unresolved
no resolver link, observed 2026-07-14T05:58:32.336515Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-07-14T05:58:32.336515Z digest=sha256:840c30a4185f594494aa89dfb1b11e5a4bb37c38494f93d214937e0e36410f00