{"as_of":"2026-08-20T15:35:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:8929f3405fe6ae14d5f83ff380d8820765545a1b29de26a8437f3a7f813f46ab","coverage":[{"denominator":49,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":49,"source":"paper_references, paper_reference_links","source_observed_at":"2026-05-25T13:57:44.931288Z","state":"measured"},{"denominator":50,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":50,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-20T06:33:59.587034+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T17:09:37.967943Z","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":"2026-08-12T17:09:37.997534Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"cited_work":{"arxiv_id":"1906.12125","doi":null,"metadata_source":"pith","pith_arxiv_id":"1906.12125","snapshot_observed_at":"2026-08-12T17:09:37.997534Z","title":"High-dimensional principal component analysis with heterogeneous missingness","venue":"stat.ME","work_id":"9da5fb8d-600c-4fb2-92a3-511dd65b1a94","year":2019},"citing_paper":{"arxiv_id":"2411.12965","last_updated":"2025-09-01T20:44:58Z","snapshot_observed_at":"2026-08-20T05:06:44.324146Z","submitted_at":"2024-11-20T01:40:53Z","title":"Two-Sided Nearest Neighbors: An adaptive and minimax optimal procedure for matrix completion","version":2},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-08-12T17:09:37.967943Z"},"links":{"cited_paper":"/paper/1906.12125","citing_paper":"/paper/2411.12965"},"observation_digest":"sha256:e41965eb4f3d772df3a590e54eff2520624c6f9dd8a78bc613fea3ad1e7ff266","observation_id":"c5a84adf-26ca-4f43-9aa8-6e8f3c7114bf","resolution":{"observed_at":"2026-08-12T17:09:38.003228Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/1906.12125/citation-record","integrity":"/paper/1906.12125/integrity","json":"/paper/1906.12125/citation-record.json","paper":"/paper/1906.12125"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"a70c76aa-86c5-4964-81aa-8db8b093f2d5","year":1957},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":1,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:e4de115a9f942ac71e9f15b5f88c3f01e939ce0a5f7f2e3b7d01c01f7ba6d751","observation_id":"8f792fcf-eda3-4084-95f8-9bb1caacd855","resolution":{"observed_at":"2026-05-25T14:00:55.006832Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Tsybakov, A","venue":null,"work_id":"7639d385-7167-4079-a856-bea0e779e8d5","year":2017},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":2,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:4b819f70b42d111b7ef53f274b3e74d7824ff0e6f0424e6992f7df6c9e6b3c60","observation_id":"af90d29e-f9cd-4d8e-a3e7-ffb345bce8ff","resolution":{"observed_at":"2026-05-25T14:00:54.999142Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Massart, P","venue":null,"work_id":"ac0e75e2-e90f-44d7-9d05-a905b183581b","year":2013},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":3,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:d8d0ca5dc83b965467c6e376b1ebea793e16c3c8665db7724d03bccdd7ff82c7","observation_id":"47eeb9c0-ea9f-4a01-9101-8ad5672aab08","resolution":{"observed_at":"2026-05-25T14:00:54.995898Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"48ef1920-7946-4f38-816c-60421347c5fe","year":null},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":4,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:e1bc9d6276906a53fbe09e39500bb24213f5cec8c9464a88a3e4f43475248e25","observation_id":"df782a6e-c25d-4df6-817e-13508b801fbf","resolution":{"observed_at":"2026-05-25T14:00:55.003335Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1804.03018","last_updated":"2018-04-09T14:23:07Z","snapshot_observed_at":"2026-08-14T19:27:53.444741Z","submitted_at":"2018-04-09T14:23:07Z","title":"High-dimensional Linear Discriminant Analysis: Optimality, Adaptive Algorithm, and Missing Data","version":1},"cited_work":{"arxiv_id":"1804.03018","doi":null,"metadata_source":"pith","pith_arxiv_id":"1804.03018","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"High-dimensional Linear Discriminant Analysis: Optimality, Adaptive Algorithm, and Missing Data","venue":"stat.ME","work_id":"653b0cda-4642-47db-a641-7c8cbc64990e","year":2018},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":5,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"cited_paper":"/paper/1804.03018","citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:79b7bcc1999fe78b7589b82bd61cd99661b13ddcd61bbbe0217844bb2e480757","observation_id":"f9471e10-ab54-4614-82f5-9573c44fc82b","resolution":{"observed_at":"2026-05-25T14:00:53.686351Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"J., Li, X., Ma, Y","venue":null,"work_id":"e0d88887-17e6-4329-a0f0-9f0d9b31c4e6","year":2011},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":6,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:b03a27d31332992baf897fbaa08bc656a5287260428480d72410f70bbdd5396b","observation_id":"e8656d55-fa04-4e85-968a-ae45405dbc03","resolution":{"observed_at":"2026-05-25T14:00:55.010011Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"43d6e538-4ec5-429a-842b-178945e51c53","year":2010},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":7,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:f16f38cf0b1f85fd8ce0cb4269e9782f38cc637d181eec3ea8febd598e281a0b","observation_id":"64ae1bfe-9a9a-469b-8c66-69de2715b6fe","resolution":{"observed_at":"2026-05-25T14:00:54.979915Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"c60b9c5c-4d56-4553-8416-a2133d1be89c","year":2009},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":8,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:9c729f20b3b33f39a4690d54c6a114e29b62cfc9e551773c903c4ee2dacbb848","observation_id":"8b571808-fef0-40a4-b658-f1e4df23d5b1","resolution":{"observed_at":"2026-05-25T14:00:54.982546Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Priebe, C","venue":null,"work_id":"42bf3b61-8234-4e21-ae0f-e4a04ed0206e","year":2018},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":9,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:e721d3cdc76ba083b4a85b5dfd0634a6cfe45c853d009a653cbfddce3bf3ef68","observation_id":"e41ec08b-22f4-4eeb-acc1-9e03f297845e","resolution":{"observed_at":"2026-05-25T14:00:54.986936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1809.09573","last_updated":"2019-09-19T17:00:59Z","snapshot_observed_at":"2026-08-14T18:23:44.330923Z","submitted_at":"2018-09-25T16:21:07Z","title":"Nonconvex Optimization Meets Low-Rank Matrix Factorization: An Overview","version":3},"cited_work":{"arxiv_id":"1809.09573","doi":null,"metadata_source":"arxiv_reference","pith_arxiv_id":"1809.09573","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Chen Y","venue":null,"work_id":"7f30ff51-13b8-4b15-9f79-0a7e9ad56290","year":2018},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":10,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"cited_paper":"/paper/1809.09573","citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:1f63e198f53146186765dd5ff2bd82f2a9ee7457249e501f3cb245ed852417f6","observation_id":"f8274926-19c3-453a-aed5-b4668b5cc9bc","resolution":{"observed_at":"2026-05-25T14:00:53.695683Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Rohe, K","venue":null,"work_id":"bc9dcc0d-3266-4c39-b052-16b146413de2","year":2017},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":11,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:450539cbe275ee5d85b892c2d598ce6c2f68aa7f1b08c23576b1510647ec33d1","observation_id":"b0d38a20-6786-4c58-b643-0e66288e7320","resolution":{"observed_at":"2026-05-25T14:00:54.974403Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"P., Laird, N","venue":null,"work_id":"f609151b-6400-4b45-b2b5-9bde571b0eec","year":1977},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":12,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:c266c908b39029ab556c35d9b64e9efe8bdd6d2c1331b5a758c3b6e008b5e4a8","observation_id":"c9c20b29-f5f2-457f-8058-480e601ffc94","resolution":{"observed_at":"2026-05-25T14:00:54.977338Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Josse J","venue":null,"work_id":"08d0e159-c469-4c95-973e-199adfd0adf7","year":2015},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":13,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:ba006306a3e7240efab0d4618a4eaf93f201446df46d3d51acf1463ad1a82a91","observation_id":"3aad30d7-c89e-4736-9141-76d4a3ca63d2","resolution":{"observed_at":"2026-05-25T14:00:54.992668Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1811.10443","last_updated":"2018-11-26T15:24:31Z","snapshot_observed_at":"2026-08-14T17:53:17.650698Z","submitted_at":"2018-11-26T15:24:31Z","title":"Sparse spectral estimation with missing and corrupted measurements","version":1},"cited_work":{"arxiv_id":"1811.10443","doi":null,"metadata_source":"pith","pith_arxiv_id":"1811.10443","snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"Sparse spectral estimation with missing and corrupted measurements","venue":"math.ST","work_id":"9a5e1fc4-3151-4583-81df-0e6b26605cae","year":2018},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":14,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"cited_paper":"/paper/1811.10443","citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:ba0292f9cbbf3032bd485347ba63e1cf6b401a1559dd7ae15df764260de7a8ec","observation_id":"ca2889a0-5ab9-4a5c-b829-fbeabc8fba45","resolution":{"observed_at":"2026-05-25T14:00:53.701889Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"edf66b8c-1c4a-4432-8398-8b8542dbb15f","year":null},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":15,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:fbe94327be13d0b5de2fac99ea9d9f011355d354a2bc7b99497a808462670ae4","observation_id":"2c6e937c-979a-4afd-8b3b-7b82e4b9fd16","resolution":{"observed_at":"2026-05-25T14:00:54.965448Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"2ea7220c-1d08-42f3-97c4-a4c567ca55c7","year":1983},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":16,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:9d661059e1dfc4f8ec0945ecb9fb82de02e9f18c931930aa6004856f9c003e12","observation_id":"80b62502-1993-4c59-be52-96e465d5e887","resolution":{"observed_at":"2026-05-25T14:00:54.960456Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"20527679-2759-4a17-ad35-5371c1eb69f7","year":2016},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":17,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:87c52060da909d4f07b08006f0d339cef000284b529e40d34dc2e1456e72fd9d","observation_id":"3898c985-b02f-4b33-9ba8-2c7206d3945a","resolution":{"observed_at":"2026-05-25T14:00:54.962924Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"70787a15-ec82-48c8-89f6-f8074f6921fe","year":2015},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":18,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:fdfe403b580766ee1d2a4677419e14d1f31e21fb1b3d456a56df6c581aa66dc6","observation_id":"9d2a6fec-94e5-4067-a2f6-ac026be8e9cb","resolution":{"observed_at":"2026-05-25T14:00:54.968442Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Sharakhmetov, S","venue":null,"work_id":"e925e710-6ea7-4869-be97-4ec2199cf6ca","year":1998},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":19,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:800054d96919fe81d0e13b561ca9d27abe5963c162ac5337ffc5a9c1afe1f0c9","observation_id":"06cfa5bc-46d5-4e89-b2c9-87f33290bd5a","resolution":{"observed_at":"2026-05-25T14:00:54.957145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"f4171247-d28d-4656-a1c7-6ed1cb5103f5","year":2009},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":20,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:fdf648f0b3627e778a8b7368032ce68d72245eb2a4dfda8d4746a3f78fa9ff03","observation_id":"694f98c7-8809-46c4-b702-cab05718d017","resolution":{"observed_at":"2026-05-25T14:00:54.950560Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Husson F","venue":null,"work_id":"d2b40ef4-0071-4f25-9899-eadcefecb84a","year":2012},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":21,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:1c34eb11bc6cf4a8ce429d8f30b7ff0a59c6022eb51ef7cc07315b8f713fa092","observation_id":"f9e924f9-6461-4a59-ada3-b8a801846ec2","resolution":{"observed_at":"2026-05-25T14:00:54.971324Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Husson F","venue":null,"work_id":"e0b46dfd-1f35-426c-b93a-120eb1c03a87","year":2009},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":22,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:ffc8e56fb0a28373fd80241db6b274fc3f2c5bfd5ab588057e36d3bae6fb4161","observation_id":"d7f6a61b-fe1c-4e41-bb80-302617c806a3","resolution":{"observed_at":"2026-05-25T14:00:54.953676Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"H., Montanari, A","venue":null,"work_id":"220b9d5f-e18f-4ce0-accf-92f74486dc3b","year":2010},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":23,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:839dc5001ac52f173d41de3f3280ac20f80bb1de75bea44fce392cc402907b82","observation_id":"da5b3bd1-210d-4419-b062-da3b13e03a6f","resolution":{"observed_at":"2026-05-25T14:00:55.012936Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"7cf4cbaa-ac3e-4ac3-a9ce-8432db00c19b","year":1997},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":24,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:2052e445052b74aa366d8ce0d2f146d9aface70610663a9b7b0bae0c5cc4e0fb","observation_id":"04eb37b6-5a09-4721-a08f-c989ff9b1542","resolution":{"observed_at":"2026-05-25T14:00:54.943615Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Tsybakov, A","venue":null,"work_id":"30dd9a85-33cc-4aa0-92b1-93b2e0a990b9","year":2011},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":25,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:ff170263f545ceaea4ba57f1084aca44bf7c5cd43a7f6d50ab267ca270fb960f","observation_id":"b5db131f-f1c6-4cc7-8f5e-72c98eb5f253","resolution":{"observed_at":"2026-05-25T14:00:54.940171Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"6db108c8-f03a-4976-81c2-5c77d389c309","year":2014},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":26,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:3a63031814fb5c27b76a2fd1009fc5dd3e80e790a1ca749fd5eff13df8e5a420","observation_id":"29abfccc-40af-485a-9b0d-37069ed7e993","resolution":{"observed_at":"2026-05-25T14:00:55.015964Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Tan, X","venue":null,"work_id":"ba678b7d-40ca-42f0-b6f8-a98dc566350b","year":2018},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":27,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:aa837e4b8aa891816e1663145bbcb9adcdd32e8163189d8f78306fb022efdecf","observation_id":"e45900f6-4a19-4a78-a28c-eafebdf112bc","resolution":{"observed_at":"2026-05-25T14:07:04.376596Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Wainwright, M","venue":null,"work_id":"d6bee512-dff4-4805-8b23-3b56c60613af","year":2012},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":28,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:35aeec24df98bf18e2f7b3cc27e83df35b99ffc7d4d3dbaa73be2cbcb5cccd81","observation_id":"3050e988-2386-433e-b322-a2f9b14e02e2","resolution":{"observed_at":"2026-05-25T14:07:04.384540Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"(2013) Sparse principal component analysis with missing observations","venue":null,"work_id":"a02828f5-1df2-44fe-85c8-46514ff6fb92","year":2013},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":29,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:20f8cd2fb5c5588290f97af76c4a996e5834db4a4ec866834dcaef693b3f45c7","observation_id":"a7eefc2f-4b0d-4213-b9f6-adfad99910ff","resolution":{"observed_at":"2026-05-25T14:00:55.038300Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"(2014) High-dimensional covariance matrix estimation with missing observations","venue":null,"work_id":"f0164c89-289a-4f34-88f1-1d43235f40f0","year":2014},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":30,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:44fe7b2f0954cee922cdcbae5adba2bab550bce56808b691d9b5cf52acc842ee","observation_id":"efa03abe-947c-4158-9647-5384b3c0f41a","resolution":{"observed_at":"2026-05-25T14:07:04.373278Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"(2007) Concentration Inequalities and Model Selection, Springer, Berlin","venue":null,"work_id":"45d2b1ef-6dd2-4d8d-9b81-058642a3c964","year":2007},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":31,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:4b850f66521cf558716997ddbda4e73c3ebb83189503be28496f90ebcbaee9b0","observation_id":"546bce36-beb5-49ca-81f1-5f3486e2f8bf","resolution":{"observed_at":"2026-05-25T14:07:04.380880Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Tibshirani R","venue":null,"work_id":"203fdfab-a777-4fc4-a1a5-de3fc1dde59d","year":2010},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":32,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:ba9c336a1d710aece283654e21a1628fdf3b42cc07d46f013ba96cb8d34e8486","observation_id":"9fa6dbe8-1ee2-402b-a77d-ecebae18b8a5","resolution":{"observed_at":"2026-05-25T14:00:55.032824Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Wainwright, M","venue":null,"work_id":"0c3380fc-8262-4077-9a26-129c38ddab6c","year":2012},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":33,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:c50269d88816de3b2f219db1fc8a1abc359693feed6a6d935f6a80c45a254af7","observation_id":"4a4529cc-edea-47cb-bab9-2d49c91da36d","resolution":{"observed_at":"2026-05-25T14:00:55.024826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Yu, B","venue":null,"work_id":"43d12610-30ef-4a06-bd8b-1bc2f39263eb","year":2011},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":34,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:77d160c25d6132699e2aa15bae22e342a02db9a15ef42943a52c9070b1870163","observation_id":"27897aa3-2c4d-4606-96d0-eb46da5371b9","resolution":{"observed_at":"2026-05-25T14:00:55.027284Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Tsybakov, A","venue":null,"work_id":"ed9c400c-b4e5-484b-9cb0-37b9d1815fc2","year":2010},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":35,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:d777de7c52e4e644fa71ebf7c2ecb1a06c43848302781e4ceb5e70d1524cf0d9","observation_id":"8fe61936-a3db-4f65-a9a1-cd72f8118a3b","resolution":{"observed_at":"2026-05-25T14:00:55.035514Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"(1970) On the subspaces of L_p ( p> 2 ) spanned by sequences of independent random variables","venue":null,"work_id":"a7f78286-c492-4620-b826-56f278cdf02e","year":1970},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":36,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:cecd1afcb6900fe92ef53c897aa7c915c238fad71cd56a813676f2d98ffca7d6","observation_id":"a4abf386-b2f8-444e-a309-98da681c0519","resolution":{"observed_at":"2026-05-25T14:07:04.362141Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"a94326f0-c4ef-4616-a3cb-e72a1369c8d6","year":2004},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":37,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:8f3ebc5a104bf3dbd131a4c7b72356ee953aef3fc2bfd4f37664c6407ac07c67","observation_id":"dd137bd2-558f-480e-92c5-715175b5a4e4","resolution":{"observed_at":"2026-05-25T14:07:04.365480Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Marron, J","venue":null,"work_id":"9d9c7f23-e6ad-402f-993b-cbe3b04c5c48","year":2016},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":38,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:be783b05aea8092682d74d583b6135fc242ad69ac63a5e3816f736e136e4ba22","observation_id":"ded0b0e5-dc19-43be-bae3-58afcb7f9dcd","resolution":{"observed_at":"2026-05-25T14:07:04.369145Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"c5cb68d4-5af8-402c-a8a7-7c4cc447d4b7","year":2012},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":39,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:f3d3502a8a927b0363e281e02d54f7f36bcdacd5fa7a1781dd5aa12c5ef0f7e0","observation_id":"c5586048-7fd5-4530-b6c9-ab8c667a4d26","resolution":{"observed_at":"2026-05-25T14:00:55.029845Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"471079a9-0a5e-4437-a841-cd921abeed6e","year":1996},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":40,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:2463fdc2c4de35b335a8e2b0de7c424ef8badd73fa7773bc4d4eaaaec6059e30","observation_id":"b8512770-ece0-4e55-a9ca-ee01dc862da1","resolution":{"observed_at":"2026-05-25T14:00:55.018878Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"(2012) Introduction to the non-asymptotic analysis of random matrices","venue":null,"work_id":"de0791c5-012f-4536-8665-c5b243c25455","year":2012},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":41,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:4483bffbc127841cf88a724d57b0d3d31951f95b2067323d6fd3e89e4dda1afa","observation_id":"d3f085f8-1fc2-40bc-948e-7c4b8d3f6900","resolution":{"observed_at":"2026-05-25T14:00:55.022123Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Plan, Y","venue":null,"work_id":"750c3a83-e21b-49ec-b20e-b0af9e28f78b","year":2016},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":42,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:e24461df60b28c28a8e32937fa578e2d48af882326c4b8fcbcc11ce615a7608b","observation_id":"3bd8334a-3970-4295-90ba-f430ad4b5228","resolution":{"observed_at":"2026-05-25T14:00:54.930809Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":null,"venue":null,"work_id":"3560b948-3548-402c-9254-7714974565e9","year":2019},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":43,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:a3cb6d5ab25f68bdd604d6e3ccc04be140f4cb451fd6f92816021cc6e1c009e6","observation_id":"e379fc0e-fca1-4e0a-ab6a-f4f31b9a4c9e","resolution":{"observed_at":"2026-05-25T14:00:54.937077Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Fan, J","venue":null,"work_id":"8c0433c3-b19b-4cd2-9d72-9dbf3199b300","year":2017},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":44,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:b4a3155ba5572d7f23fe55632135c550aa047fa9f93a362df517ebbea88aed21","observation_id":"2ab8cd21-a4ca-4e32-90ef-f1b6125bb7c6","resolution":{"observed_at":"2026-05-25T14:00:54.933962Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Lyttkens, E","venue":null,"work_id":"798d651b-4ca0-4ab5-8896-3a7885972af4","year":1969},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":45,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:ca5389acaf064b5a11eee0cb7c1cfd2f93a96a3293ea2711b09d8bb1655240ef","observation_id":"d59059a3-ba6b-4e6d-9ea1-bb50d4ed1cea","resolution":{"observed_at":"2026-05-25T14:00:54.922038Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"(1997) Assouad, Fano and Le Cam","venue":null,"work_id":"ace78244-7407-4dc2-8387-6557322279d5","year":1997},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":46,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:be9910f93f327a2b6eedcb95d456d8bc83fa89fd2b22b94c682d3cce87990477","observation_id":"ed63e3e2-353c-495d-9f05-44aedd009de6","resolution":{"observed_at":"2026-05-25T14:00:54.928015Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Samworth, R","venue":null,"work_id":"41131ac5-0d63-43df-be15-0ed9fb152e97","year":2015},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":47,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:e6839a1ae5ccd832cae54cc877b16868bb5805a8c1e333f1b048d38231b6412b","observation_id":"c9ac827c-42a5-4fcc-8a62-1d6d7e311df3","resolution":{"observed_at":"2026-05-25T14:00:54.924875Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.08316","last_updated":"2021-04-01T14:00:56Z","snapshot_observed_at":"2026-08-18T06:57:47.853980Z","submitted_at":"2018-10-19T00:22:25Z","title":"Heteroskedastic PCA: Algorithm, Optimality, and Applications","version":3},"cited_work":{"arxiv_id":"1810.08316","doi":"10.48550/arxiv.1810.08316","metadata_source":"pith","pith_arxiv_id":"1810.08316","snapshot_observed_at":"2026-08-05T02:28:24.338817Z","title":"Heteroskedastic PCA: Algorithm, Optimality, and Applications","venue":"math.ST","work_id":"d64b2cc2-6f0b-4d3e-840a-e2e8b70771e7","year":2018},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":48,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"cited_paper":"/paper/1810.08316","citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:bb5c0770c87c920376f895230aa25f8198c22c932f838cb59eef9f29423a9082","observation_id":"3af8b87f-ea8e-4aa4-8745-fe4aa92e427a","resolution":{"observed_at":"2026-05-25T14:00:53.691027Z","resolver_source":"arxiv_id","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-07-10T00:48:54.324534+00:00","source":"crossref_status_cache"},{"observed_at":"2026-07-10T00:48:54.324534+00:00","source":"openalex_status_cache"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-06-05T21:23:00.469572Z","title":"and Samworth, R","venue":null,"work_id":"46c7a42b-7514-47f4-aa83-56de0e5612f7","year":2019},"citing_paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness","version":1},"reference_index":49,"source":"arxiv_source","source_observed_at":"2026-05-25T13:57:44.931288Z"},"links":{"citing_paper":"/paper/1906.12125"},"observation_digest":"sha256:1d5984cbaa41283e7123b92a6da2b4e0cc1ed91c345cef767daef67af4c62e46","observation_id":"88a050b3-2e26-452f-b593-38cf752c4e27","resolution":{"observed_at":"2026-05-25T14:00:54.947181Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-20T06:33:59.587034+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1906.12125","last_updated":"2019-06-28T10:37:16Z","latest_version":1,"primary_category":"stat.ME","snapshot_observed_at":"2026-08-18T18:14:57.430940Z","submitted_at":"2019-06-28T10:37:16Z","title":"High-dimensional principal component analysis with heterogeneous missingness"},"reference_resolution":{"displayed":49,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":15,"verified_exact":4,"verified_fuzzy":30},"total_outbound_references":49},"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":"2026-08-20T06:33:59.587034+00:00","source":"crossref"},{"observed_at":"2026-08-20T06:33:54.927442+00:00","source":"retraction_watch"}],"thesis":"As of 20 August 2026, this Paper Citation Record lists 49 of 49 outbound references and 1 inbound Pith citation observation for arXiv:1906.12125."}