Typed states for the displayed outbound observations.
Source: paper_references, paper_reference_links, observed 2026-08-12T12:01:53.440488Z
Paper Citation Record · LEDGER
Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates
As of 13 August 2026, this Paper Citation Record lists 15 of 15 outbound references and 0 inbound Pith citation observations for arXiv:2411.17618.
A citation records a reference. It does not transfer a finding from one paper to another.
Coverage vector
One-hop event checks from named stored sources.
Source: scholarly_work_events, retraction_status_cache, observed 2026-08-13T06:32:02.005865+00:00
Pith citing papers itemized under the disclosed page cap.
Source: paper_references, paper_reference_links
A source-named dated measurement, never combined with another source.
Source: cited_works
Reference resolution
15 of 15 outbound references displayed
- verified exact0
- verified fuzzy12
- unresolved2
- parse uncertain0
- malformed identifier0
- metadata mismatch1
External citation measurements
No source-named external measurement is stored.
Outbound references
Observation 5e1af3e3-e86a-4240-acf5-f818026b3c37 · outbound
This paper cites Causal in ference in high di- mensions: a marriage between Bayesian modeling and good frequent ist properties,.
Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates Causal in ference in high di- mensions: a marriage between Bayesian modeling and good frequent ist properties,
Reference 1
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:01:53.844956Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 16aa0aa3-9192-4ab0-9615-522123f17fcd · outbound
This paper cites Let |hi(t)| ≤ |tTWi|, K/4 > ¯σ2 = sup t∈T ¯E[hi(t)2ξ2 i ], ˜p = dim(Wi), and ‖T ‖1 = sup t∈T ‖t‖1.
Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates Let |hi(t)| ≤ |tTWi|, K/4 > ¯σ2 = sup t∈T ¯E[hi(t)2ξ2 i ], ˜p = dim(Wi), and ‖T ‖1 = sup t∈T ‖t‖1
Reference 2
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:01:53.602481Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 8885db3f-6973-4bd6-9ecf-3f5306f9a3a7 · outbound
This paper cites Large-scale Baye sian logistic regression for text categorization,.
Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates Large-scale Baye sian logistic regression for text categorization,
Reference 5
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:01:53.779798Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 3a41afec-059a-41dd-919c-a36a9b2b3db7 · outbound
This paper cites an unresolved cited work.
Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates Unresolved cited work
Reference 7
- Resolution
- unresolved
- raw_fallback, observed 2026-08-12T12:01:53.581434Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation db03dbd2-2b92-4091-ae42-1acf90e9bd5e · outbound
This paper cites Lemma 1 consists of preliminary results shown in ( Walker, 1969; Schervish, 2012).
Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates Lemma 1 consists of preliminary results shown in ( Walker, 1969; Schervish, 2012)
Reference 12
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:01:53.625768Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 8bb2650a-4d9b-4c6a-b3c9-ea9ac5d53cde · outbound
This paper cites (B.22) From Walker (1969), we already know how to control the ˆθ0 related term.
Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates (B.22) From Walker (1969), we already know how to control the ˆθ0 related term
Reference 15
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:01:53.560920Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 3db884d0-5552-4e02-bf66-890a0914e85d · outbound
This paper cites Does maternal birth out come differentially influ- ence the occurrence of infant death among African Americans and European Americans?.
Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates Does maternal birth out come differentially influ- ence the occurrence of infant death among African Americans and European Americans?
Reference 66
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:01:53.707490Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 2ea22dcb-6f86-42ee-aa05-4a04a815b384 · outbound
This paper cites Effects of model selection on inferenc e,.
Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates Effects of model selection on inferenc e,
Reference 108
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:01:53.653835Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation b964e1f3-3db2-4b56-b78c-c0c704df6d43 · outbound
This paper cites LASSO Methods for Gaussian Instrumental Variables Models.
Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates LASSO Methods for Gaussian Instrumental Variables Models
Reference 119
- Resolution
- unresolved
- no resolver link, observed 2026-08-12T12:01:53.335543Z
Source-reported events for the cited work
Unavailable: canonical work link unavailable.
Observation 584b4382-908b-428c-aeba-c1f558c009f6 · outbound
This paper cites Honest variable selection in linear and logistic reg ression models via l1 and l1+ l2 penalization,.
Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates Honest variable selection in linear and logistic reg ression models via l1 and l1+ l2 penalization,
Reference 232
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:01:53.802673Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation de456766-d346-4773-a7c0-ded1934c68e0 · outbound
This paper cites Inference for multiple treatment effects using confounder importance learning.
Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates Inference for multiple treatment effects using confounder importance learning
Reference 688
- Resolution
- metadata mismatch
- local_arxiv, observed 2026-08-12T12:01:53.500151Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 639dd909-2fdf-4389-a0c8-59b1758fdb70 · outbound
This paper cites Confidence intervals a nd hypothesis testing for high-dimensional regression,.
Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates Confidence intervals a nd hypothesis testing for high-dimensional regression,
Reference 773
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:01:53.756594Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 794f96fb-5b2f-4175-ba0f-33bb391efbd9 · outbound
This paper cites Skinny Gibbs: A Cons istent and Scalable Gibbs Sampler for Model Selection,.
Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates Skinny Gibbs: A Cons istent and Scalable Gibbs Sampler for Model Selection,
Reference 817
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:01:53.680381Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 495e12b5-5eff-4851-b356-9c22c22101a9 · outbound
This paper cites Consistent high-dimensiona l Bayesian variable selection via penalized credible regions,.
Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates Consistent high-dimensiona l Bayesian variable selection via penalized credible regions,
Reference 1490
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:01:53.824610Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Observation 5bb27fb1-b5e0-4d43-a393-dd105f9e7725 · outbound
This paper cites Non-asymptotic oracle inequalities for the La sso and group Lasso in high dimensional logistic model,.
Valid Bayesian Inference based on Variance Weighted Projection for High-Dimensional Logistic Regression with Binary Covariates Non-asymptotic oracle inequalities for the La sso and group Lasso in high dimensional logistic model,
Reference 2981
- Resolution
- verified fuzzy
- raw_fallback, observed 2026-08-12T12:01:53.731571Z
Source-reported events for the cited work
No event found in the named queried sources as of 2026-08-13T06:32:02.005865+00:00.
Pith citing papers
No inbound Pith citation observations are available.