Paper Citation Record · LEDGER

Bernstein - von Mises Theorem for growing parameter dimension

As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 7 inbound Pith citation observations for arXiv:1302.3430.

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

pith.paper-citation-record.v1
1302.3430 v4

Coverage vector

measured 0 of 0 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links

measured 7 of 7 standing notices

One-hop event checks from named stored sources.

Source: scholarly_work_events, retraction_status_cache, observed 2026-08-19T06:32:44.657259+00:00

measured 7 of 7 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-15T17:32:37.889186Z

measured 0 of 1 external citation measurements

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

Source: pith, observed 2026-05-19T21:12:47.284915Z

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 a25ee269-4305-41d2-9971-4537ea204e52 · inbound

On rank estimators in increasing dimensions cites this paper.

On rank estimators in increasing dimensions Bernstein - von Mises Theorem for growing parameter dimension

Reference 44

Resolution: unresolved; no resolver link, observed 2026-08-14T13:26:26.823066Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation b9b8bb3a-98b7-4184-a601-3017a7a1c024 · inbound

CLT in high-dimensional Bayesian linear regression with low SNR cites this paper.

CLT in high-dimensional Bayesian linear regression with low SNR Bernstein - von Mises Theorem for growing parameter dimension

Reference 67

Resolution: unresolved; no resolver link, observed 2026-08-06T10:59:57.221879Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 04c40705-05f6-41ec-9e98-fefdeb504808 · inbound

Factor Augmented Quantile Regression Model cites this paper.

Factor Augmented Quantile Regression Model Bernstein - von Mises Theorem for growing parameter dimension

Reference 28

Resolution: unresolved; no resolver link, observed 2026-08-06T10:22:45.821747Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 9e0906e5-eab6-49f4-b39d-3d5e0ab2e4c3 · inbound

Robust Data Fusion via Subsampling cites this paper.

Robust Data Fusion via Subsampling Bernstein - von Mises Theorem for growing parameter dimension

Reference 31

Resolution: unresolved; no resolver link, observed 2026-08-15T17:32:37.889186Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 15a128df-d5a3-4926-8892-af7ad1756485 · inbound

Sparse Rank Regression for Restricted-Access Economic Data cites this paper.

Sparse Rank Regression for Restricted-Access Economic Data Bernstein - von Mises Theorem for growing parameter dimension

Reference 26

Resolution: metadata mismatch; arxiv_id, observed 2026-05-12T08:46:25.702595Z

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.

Observation 7645d1be-94c9-4504-b5e8-7d71152ddcf3 · inbound

Stochastic Optimization and Data Science cites this paper.

Stochastic Optimization and Data Science Bernstein - von Mises Theorem for growing parameter dimension

Reference 83

Resolution: metadata mismatch; local_arxiv, observed 2026-05-19T21:12:47.286589Z

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.

Observation 97b23852-b067-43fb-8817-ce29e6213d6d · inbound

Distributed and recursive Bayesian inference for Big Data and complex spatio-temporal models cites this paper.

Distributed and recursive Bayesian inference for Big Data and complex spatio-temporal models Bernstein - von Mises Theorem for growing parameter dimension

Reference 78

Resolution: unresolved; no resolver link, observed 2026-07-30T23:20:10.422888Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.