Paper Citation Record · LEDGER

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data

As of 20 August 2026, this Paper Citation Record lists 22 of 22 outbound references and 2 inbound Pith citation observations for arXiv:2504.19924.

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

pith.paper-citation-record.v1
2504.19924 v2

Coverage vector

measured 22 of 22 reference resolution

Typed states for the displayed outbound observations.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:47:38.775539Z

measured 24 of 24 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 2 of 2 inbound itemization

Pith citing papers itemized under the disclosed page cap.

Source: paper_references, paper_reference_links, observed 2026-08-16T05:47:38.673175Z

measured 0 of 1 external citation measurements

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

Source: arxiv_reference, observed 2026-05-12T08:46:25.704712Z

Reference resolution

22 of 22 outbound references displayed

verified exact0
verified fuzzy19
unresolved3
parse uncertain0
malformed identifier0
metadata mismatch0

External citation measurements

No source-named external measurement is stored.

Outbound references

Observation 0a1e3698-eef3-44be-8d0a-93759cafc34b · outbound

This paper cites Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data.

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data

Reference 1

Resolution: unresolved; no resolver link, observed 2026-08-16T05:47:38.673175Z

Source-reported events for the cited work

Unavailable: canonical work link unavailable.

Observation 30b4ee1c-3275-4b41-b4c3-2460c5e49051 · outbound

This paper cites Assumption.

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data Assumption

Reference 2

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:47:39.049805Z

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 ff2b37d6-727e-446a-81be-bf68752cbf54 · outbound

This paper cites an unresolved cited work.

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data Unresolved cited work

Reference 3

Resolution: unresolved; raw_fallback, observed 2026-08-16T05:47:39.121889Z

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 6c5ff9a6-ffc7-4832-a317-d506cea6c1a7 · outbound

This paper cites For hypotheses where the DC test yields significantp-values (i.e., below 0.05), the CST show even lowerp-values.

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data For hypotheses where the DC test yields significantp-values (i.e., below 0.05), the CST show even lowerp-values

Reference 4

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:47:38.879347Z

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 23c6e96f-c4e9-462c-ac32-b79b56a6c613 · outbound

This paper cites Taylor’s expansion.

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data Taylor’s expansion

Reference 5

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:47:39.135625Z

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 55567816-9bbf-4201-b390-31c3ade26762 · outbound

This paper cites 13 Assumption 4 (Homogeneity).

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data 13 Assumption 4 (Homogeneity)

Reference 6

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:47:39.078970Z

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 ea8739c4-b962-4f83-89fe-982ebbfcc2e7 · outbound

This paper cites Assumption 4 introduces a homogeneity condition to control variations in loss functions across local data sites, akin to the conditions in Fan et al.

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data Assumption 4 introduces a homogeneity condition to control variations in loss functions across local data sites, akin to the conditions in Fan et al

Reference 7

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:47:39.064508Z

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 029802f8-9efe-4914-954a-d606f5c91890 · outbound

This paper cites The positive definiteness of the Hessian essentially ensures thatb′′(xT iβ∗) is bounded away from zero with high probability, which is mild for sub-Gaussian designs.

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data The positive definiteness of the Hessian essentially ensures thatb′′(xT iβ∗) is bounded away from zero with high probability, which is mild for sub-Gaussian designs

Reference 8

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:47:38.974865Z

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 0dd095ec-1189-4a45-a5e8-95c9d9b8e072 · outbound

This paper cites It limits our consideration to local alternatives with a bounded radius for∥h∥2 and also requires the regularity of the constraint matrixC.

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data It limits our consideration to local alternatives with a bounded radius for∥h∥2 and also requires the regularity of the constraint matrixC

Reference 9

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:47:39.035696Z

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 da5f7cdd-7f17-4f75-aa47-524c459fb37e · outbound

This paper cites 16 For example, we can use the local ℓ1-penalized estimator computed on the master machine.

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data 16 For example, we can use the local ℓ1-penalized estimator computed on the master machine

Reference 10

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:47:39.020588Z

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 b5526ef1-b4a8-43b7-be11-2b39c4a5bc6a · outbound

This paper cites A common form of the irrepresentable condition can be formulated as follows: for a given valuea0∈ (0, 1), maxj∈Sc∥JjAJ−1 AA∥1≤a0, whereA is the true support of the specific problem.

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data A common form of the irrepresentable condition can be formulated as follows: for a given valuea0∈ (0, 1), maxj∈Sc∥JjAJ−1 AA∥1≤a0, whereA is the true support of the specific problem

Reference 11

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:47:39.005103Z

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 f6991b3b-e571-4aa2-8e70-00cac59fa472 · outbound

This paper cites an unresolved cited work.

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data Unresolved cited work

Reference 12

Resolution: unresolved; raw_fallback, observed 2026-08-16T05:47:38.990072Z

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 a3433924-13f4-4f2b-89e8-5f4136dc1cb9 · outbound

This paper cites In particular, we examine the empirical Type I error and power analysis of the CST across different kinds of linear hypotheses and collaborative settings.

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data In particular, we examine the empirical Type I error and power analysis of the CST across different kinds of linear hypotheses and collaborative settings

Reference 14

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:47:38.960053Z

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 9ef1589e-2596-4764-ae45-7eece34aedbb · outbound

This paper cites Linear regression.

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data Linear regression

Reference 15

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:47:38.944671Z

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 f294c23a-5cf9-456e-bcfe-8a13bd748991 · outbound

This paper cites In the distributed system, the company with the most trips serves as the master site, withn1 = 15, 282local samples.

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data In the distributed system, the company with the most trips serves as the master site, withn1 = 15, 282local samples

Reference 17

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:47:38.912571Z

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 737fc292-c53e-4ee3-a137-3524d13e0c78 · outbound

This paper cites Specifically, we apply the sure independent ranking and screening (Zhu et al., 2011, SIRS) across all the170 covariates using theR package VariableScreening available in CRAN.

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data Specifically, we apply the sure independent ranking and screening (Zhu et al., 2011, SIRS) across all the170 covariates using theR package VariableScreening available in CRAN

Reference 18

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:47:38.894908Z

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 e5953cb5-dadf-4c61-9302-26b38fe9fc88 · outbound

This paper cites Distributed testing and estimation under sparse high dimensional models.

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data Distributed testing and estimation under sparse high dimensional models

Reference 20

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:47:38.863101Z

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 20b57607-fe36-40f1-993a-45f2bd0be6ee · outbound

This paper cites Strong oracle optimality of folded concave penalized estimation.

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data Strong oracle optimality of folded concave penalized estimation

Reference 841

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:47:38.847128Z

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 0c70a11d-3b1d-400a-9954-195feb1f9227 · outbound

This paper cites Theℓ1 penalty (Tibshirani,.

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data Theℓ1 penalty (Tibshirani,

Reference 2001

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:47:39.093038Z

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 e0be0f88-1cd1-4f43-bdf4-89f873dfca08 · outbound

This paper cites Third, partial penalization guarantees that no penalties are imposed onθ, the parameters of interest in our hypothesis.

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data Third, partial penalization guarantees that no penalties are imposed onθ, the parameters of interest in our hypothesis

Reference 2014

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:47:39.107883Z

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 515fc8a2-ff8d-479e-8b45-6c51fc20cf0f · outbound

This paper cites This filtering process leads tom = 10 companies, representing the top ten service providers in.

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data This filtering process leads tom = 10 companies, representing the top ten service providers in

Reference 2023

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:47:38.928832Z

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 e1d2bd8a-88a4-449a-ad50-4160e39b9c85 · outbound

This paper cites A survey of tuning parameter selection for high-dimensional regression.

Collaborative Inference for Sparse High-Dimensional Models with Non-Shared Data A survey of tuning parameter selection for high-dimensional regression

Reference 3645

Resolution: verified fuzzy; raw_fallback, observed 2026-08-16T05:47:38.830982Z

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.

Pith citing papers

Observation 0a1e3698-eef3-44be-8d0a-93759cafc34b · inbound