{"as_of":"2026-08-19T16:05:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:4da911acb6c7664aa9656c8b48cfeeb0ce6d6594d662957c64d14b8a00b8715e","coverage":[{"denominator":0,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":4,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":4,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-19T06:32:44.657259+00:00","state":"measured"},{"denominator":4,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":4,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-07T12:51:37.760974Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-05-22T08:11:17.264660Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1611.06649","last_updated":"2016-12-20T01:01:25Z","snapshot_observed_at":"2026-08-14T21:29:30.413379Z","submitted_at":"2016-11-21T05:02:08Z","title":"High-Dimensional Bayesian Regularised Regression with the BayesReg Package","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.06649","snapshot_observed_at":"2026-08-07T12:51:37.760974Z","title":"Makalic and D","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2505.23320","last_updated":"2025-05-29T10:30:13Z","snapshot_observed_at":"2026-08-15T20:22:52.843799Z","submitted_at":"2025-05-29T10:30:13Z","title":"Efficient Parameter Estimation for Bayesian Network Classifiers using Hierarchical Linear Smoothing","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-08-07T12:51:37.760974Z"},"links":{"cited_paper":"/paper/1611.06649","citing_paper":"/paper/2505.23320"},"observation_digest":"sha256:8631eb69678a356e68aea236c1c90b8ae757088eb4b4a8b69c49273041aa1b73","observation_id":"f7ed7690-6c8c-4bcc-99bc-57e44b747eed","resolution":{"observed_at":"2026-08-07T12:51:37.760974Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1611.06649","last_updated":"2016-12-20T01:01:25Z","snapshot_observed_at":"2026-08-14T21:29:30.413379Z","submitted_at":"2016-11-21T05:02:08Z","title":"High-Dimensional Bayesian Regularised Regression with the BayesReg Package","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.06649","snapshot_observed_at":"2026-08-07T05:40:27.740965Z","title":"Makalic and D","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2506.07394","last_updated":"2025-08-15T23:00:59Z","snapshot_observed_at":"2026-08-18T00:33:17.848603Z","submitted_at":"2025-06-09T03:37:20Z","title":"The Lasso Distribution: Properties, Sampling Methods, and Applications in Bayesian Lasso Regression","version":3},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-08-07T05:40:27.740965Z"},"links":{"cited_paper":"/paper/1611.06649","citing_paper":"/paper/2506.07394"},"observation_digest":"sha256:3acdf27da57c2159b74f7fbd5a1d395fd11e2c273104ce5d64f6864bc7778ee2","observation_id":"c3866f3a-f312-49e2-a5e7-fd4fd7bd94ee","resolution":{"observed_at":"2026-08-07T05:40:27.740965Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1611.06649","last_updated":"2016-12-20T01:01:25Z","snapshot_observed_at":"2026-08-14T21:29:30.413379Z","submitted_at":"2016-11-21T05:02:08Z","title":"High-Dimensional Bayesian Regularised Regression with the BayesReg Package","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1611.06649","snapshot_observed_at":"2026-08-06T17:29:12.730168Z","title":"High-dimensional bayesian regularised regression with the bayesreg package,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2507.10975","last_updated":"2025-07-22T23:26:22Z","snapshot_observed_at":"2026-08-13T12:01:41.189325Z","submitted_at":"2025-07-15T04:40:51Z","title":"Robust Bayesian high-dimensional variable selection and inference with the horseshoe family of priors","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-06T17:29:12.730168Z"},"links":{"cited_paper":"/paper/1611.06649","citing_paper":"/paper/2507.10975"},"observation_digest":"sha256:6303bcc7b47ef0c510fbb78f12c56ed391f67c37de8cf72a2188bd3b76520323","observation_id":"49b56369-5fcb-4037-8a74-e0f97b637f02","resolution":{"observed_at":"2026-08-06T17:29:12.730168Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1611.06649","last_updated":"2016-12-20T01:01:25Z","snapshot_observed_at":"2026-08-14T21:29:30.413379Z","submitted_at":"2016-11-21T05:02:08Z","title":"High-Dimensional Bayesian Regularised Regression with the BayesReg Package","version":3},"cited_work":{"arxiv_id":"1611.06649","doi":null,"metadata_source":"pith","pith_arxiv_id":"1611.06649","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"High-Dimensional Bayesian Regularised Regression with the BayesReg Package","venue":"stat.CO","work_id":"987bf3a6-1601-4fcd-901c-f177d992defc","year":2016},"citing_paper":{"arxiv_id":"2605.21659","last_updated":"2026-08-03T14:49:35Z","snapshot_observed_at":"2026-08-15T05:13:08.393936Z","submitted_at":"2026-05-20T19:12:29Z","title":"Adaptive Generalized Elliptical Slice Sampling","version":1},"reference_index":62,"source":"arxiv_source","source_observed_at":"2026-05-22T08:07:51.401166Z"},"links":{"cited_paper":"/paper/1611.06649","citing_paper":"/paper/2605.21659"},"observation_digest":"sha256:ab7577d4c9359195025c4b996cf87f4f39d279e4d750777dcc5cc1d9d3a8de4d","observation_id":"b374e623-b3fe-4ca2-915e-954aa96e9da7","resolution":{"observed_at":"2026-05-22T08:11:17.267139Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-19T06:32:44.657259+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1611.06649/citation-record","integrity":"/paper/1611.06649/integrity","json":"/paper/1611.06649/citation-record.json","paper":"/paper/1611.06649"},"outbound":[],"paper":{"arxiv_id":"1611.06649","last_updated":"2016-12-20T01:01:25Z","latest_version":3,"primary_category":"stat.CO","snapshot_observed_at":"2026-08-14T21:29:30.413379Z","submitted_at":"2016-11-21T05:02:08Z","title":"High-Dimensional Bayesian Regularised Regression with the BayesReg Package"},"reference_resolution":{"displayed":0,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":0},"total_outbound_references":0},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-19T06:32:44.657259+00:00","source":"crossref"},{"observed_at":"2026-08-19T06:32:39.956319+00:00","source":"retraction_watch"}],"thesis":"As of 19 August 2026, this Paper Citation Record lists 0 of 0 outbound references and 4 inbound Pith citation observations for arXiv:1611.06649."}