Paper Citation Record · LEDGER

Progression: an extrapolation principle for regression

As of 22 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:2410.23246.

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

pith.paper-citation-record.v1
2410.23246 v1

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 4 of 4 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-22T06:32:14.747728+00:00

measured 4 of 4 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T18:55:15.167355Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-06-28T20:22:37.345874Z

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 33416c35-eb95-4ce1-81fd-8fe5078ceaa3 · inbound

Theoretical guarantees for neural estimators in parametric statistics cites this paper.

Theoretical guarantees for neural estimators in parametric statistics Progression: an extrapolation principle for regression

Reference 2024

Resolution: unresolved; no resolver link, observed 2026-08-15T18:55:15.167355Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation de220288-221b-499b-ba42-207687caa4a9 · inbound

Extrapolation in Statistical Learning with Extreme Value Theory cites this paper.

Extrapolation in Statistical Learning with Extreme Value Theory Progression: an extrapolation principle for regression

Reference 6

Resolution: verified exact; arxiv_id, observed 2026-05-11T16:31:09.455247Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 63a32a8a-1ae6-4b31-acb5-620ae224d107 · inbound

Out-of-Distribution generalization of quantile regression with heavy tailed inputs: an SVM approach cites this paper.

Out-of-Distribution generalization of quantile regression with heavy tailed inputs: an SVM approach Progression: an extrapolation principle for regression

Reference 54

Resolution: metadata mismatch; arxiv_id, observed 2026-06-28T20:22:37.347771Z

Source-reported events for the cited work

No event found in the named queried sources as of 2026-08-22T06:32:14.747728+00:00.

Observation 989ce656-abf8-45f8-a533-1b2f734b837e · inbound

Extrapolation of extreme covariates in generalized additive regression using extreme-value theory cites this paper.

Extrapolation of extreme covariates in generalized additive regression using extreme-value theory Progression: an extrapolation principle for regression

Reference 22

Resolution: unresolved; no resolver link, observed 2026-07-13T05:49:13.464834Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.