Pith. sign in

Paper Citation Record · LEDGER

Signature moments to characterize laws of stochastic processes

As of 9 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 2 inbound Pith citation observations for arXiv:1810.10971.

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

pith.paper-citation-record.v1
1810.10971 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 2026-08-09T06:31:02.800959+00:00

measured 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-07T14:07:07.916882Z

measured 1 of 1 external citation measurements

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

Source: pith, observed 2026-08-05T02:28:24.338817Z

Reference resolution

0 of 0 outbound references displayed

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

External citation measurements

43
pith, observed 2026-08-05T02:28:24.338817Z

Outbound references

No outbound reference observations are available for this paper version.

Pith citing papers

Observation 5ae5aa2f-fd4a-474d-99ad-4bda7103a2fe · inbound

Learning with Expected Signatures: Theory and Applications cites this paper.

Learning with Expected Signatures: Theory and Applications Signature moments to characterize laws of stochastic processes

Reference 6

Resolution
unresolved
no resolver link, observed 2026-08-07T14:07:07.916882Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

source=arxiv_source observed=2026-08-07T14:07:07.916882Z digest=sha256:0bd784005fc878f94356194e3298f24e35bce56fa7802dea7d7aea75f5e9802b

Observation 42dc1ea9-0d69-422b-b394-4d1c9dd624fb · inbound

How Fast Do Signatures Learn? Statistical Theory and Applications for Path Regression cites this paper.

How Fast Do Signatures Learn? Statistical Theory and Applications for Path Regression Signature moments to characterize laws of stochastic processes

Reference 2022

Resolution
verified exact
local_arxiv, observed 2026-08-01T16:59:48.334748Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.

source=pdf_text observed=2026-08-01T16:54:05.983811Z digest=sha256:f09de8cb4382976007945ed19badf76dd65d5b1cd430d2d2ad947b6dfc9d54c3