{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2011:C7WWOMCFWDUZLNJZS5VPBO5RYZ","short_pith_number":"pith:C7WWOMCF","canonical_record":{"source":{"id":"1111.1133","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2011-11-04T14:12:49Z","cross_cats_sorted":["q-fin.PM","q-fin.ST"],"title_canon_sha256":"d9134888069dec2b11fa8022a7ecceedad10d622bdc89592ffb1ac19abe27227","abstract_canon_sha256":"8f45c7db41ea3a198078b1d55d26ab4a123549d565086eb9cdda03df20f859cc"},"schema_version":"1.0"},"canonical_sha256":"17ed673045b0e995b539976af0bbb1c666cbb21f100ff19e295e10582a595fe9","source":{"kind":"arxiv","id":"1111.1133","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1111.1133","created_at":"2026-05-18T02:21:34Z"},{"alias_kind":"arxiv_version","alias_value":"1111.1133v2","created_at":"2026-05-18T02:21:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1111.1133","created_at":"2026-05-18T02:21:34Z"},{"alias_kind":"pith_short_12","alias_value":"C7WWOMCFWDUZ","created_at":"2026-05-18T12:26:26Z"},{"alias_kind":"pith_short_16","alias_value":"C7WWOMCFWDUZLNJZ","created_at":"2026-05-18T12:26:26Z"},{"alias_kind":"pith_short_8","alias_value":"C7WWOMCF","created_at":"2026-05-18T12:26:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2011:C7WWOMCFWDUZLNJZS5VPBO5RYZ","target":"record","payload":{"canonical_record":{"source":{"id":"1111.1133","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2011-11-04T14:12:49Z","cross_cats_sorted":["q-fin.PM","q-fin.ST"],"title_canon_sha256":"d9134888069dec2b11fa8022a7ecceedad10d622bdc89592ffb1ac19abe27227","abstract_canon_sha256":"8f45c7db41ea3a198078b1d55d26ab4a123549d565086eb9cdda03df20f859cc"},"schema_version":"1.0"},"canonical_sha256":"17ed673045b0e995b539976af0bbb1c666cbb21f100ff19e295e10582a595fe9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:21:34.909043Z","signature_b64":"HHtyCwYCmSK2U70denLoyaqkh/cFCDTsLprJgi7fkdTlzONNoo9fGtNmLN6lmZKRQVHNoN0NAVuYEsVWvUhDBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"17ed673045b0e995b539976af0bbb1c666cbb21f100ff19e295e10582a595fe9","last_reissued_at":"2026-05-18T02:21:34.908599Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:21:34.908599Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1111.1133","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:21:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ngfoAQ1BWGfWQHZ+ZvNHVR7mnyeVNaGDuOW6tVvZn2aqCVJ3fama6WMcEQ/tMgm+WSiyfuSxYHQ/Km4Wj31QAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-18T22:33:21.636581Z"},"content_sha256":"89f58182ec77de14d37789662ea48124ec0b69a6200436d89447259f543ebb1f","schema_version":"1.0","event_id":"sha256:89f58182ec77de14d37789662ea48124ec0b69a6200436d89447259f543ebb1f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2011:C7WWOMCFWDUZLNJZS5VPBO5RYZ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Recovering Model Structures from Large Low Rank and Sparse Covariance Matrix Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["q-fin.PM","q-fin.ST"],"primary_cat":"stat.ME","authors_text":"Xi Luo","submitted_at":"2011-11-04T14:12:49Z","abstract_excerpt":"Many popular statistical models, such as factor and random effects models, give arise a certain type of covariance structures that is a summation of low rank and sparse matrices. This paper introduces a penalized approximation framework to recover such model structures from large covariance matrix estimation. We propose an estimator based on minimizing a non-likelihood loss with separable non-smooth penalty functions. This estimator is shown to recover exactly the rank and sparsity patterns of these two components, and thus partially recovers the model structures. Convergence rates under vario"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1111.1133","kind":"arxiv","version":2},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:21:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dUIJS3ylEqtDf90aspl/KNMy76Cx+zyNa/13qJWPB4wPBQliSvqEKz3Jc7LslNyHF31LZbnakUBU7hAIjO/LAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-18T22:33:21.637248Z"},"content_sha256":"a7d2787cca5b4f713184edc12d81cde283ddc4199067bb440094727a89ddad8d","schema_version":"1.0","event_id":"sha256:a7d2787cca5b4f713184edc12d81cde283ddc4199067bb440094727a89ddad8d"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/C7WWOMCFWDUZLNJZS5VPBO5RYZ/bundle.json","state_url":"https://pith.science/pith/C7WWOMCFWDUZLNJZS5VPBO5RYZ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/C7WWOMCFWDUZLNJZS5VPBO5RYZ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-18T22:33:21Z","links":{"resolver":"https://pith.science/pith/C7WWOMCFWDUZLNJZS5VPBO5RYZ","bundle":"https://pith.science/pith/C7WWOMCFWDUZLNJZS5VPBO5RYZ/bundle.json","state":"https://pith.science/pith/C7WWOMCFWDUZLNJZS5VPBO5RYZ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/C7WWOMCFWDUZLNJZS5VPBO5RYZ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2011:C7WWOMCFWDUZLNJZS5VPBO5RYZ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"8f45c7db41ea3a198078b1d55d26ab4a123549d565086eb9cdda03df20f859cc","cross_cats_sorted":["q-fin.PM","q-fin.ST"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2011-11-04T14:12:49Z","title_canon_sha256":"d9134888069dec2b11fa8022a7ecceedad10d622bdc89592ffb1ac19abe27227"},"schema_version":"1.0","source":{"id":"1111.1133","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1111.1133","created_at":"2026-05-18T02:21:34Z"},{"alias_kind":"arxiv_version","alias_value":"1111.1133v2","created_at":"2026-05-18T02:21:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1111.1133","created_at":"2026-05-18T02:21:34Z"},{"alias_kind":"pith_short_12","alias_value":"C7WWOMCFWDUZ","created_at":"2026-05-18T12:26:26Z"},{"alias_kind":"pith_short_16","alias_value":"C7WWOMCFWDUZLNJZ","created_at":"2026-05-18T12:26:26Z"},{"alias_kind":"pith_short_8","alias_value":"C7WWOMCF","created_at":"2026-05-18T12:26:26Z"}],"graph_snapshots":[{"event_id":"sha256:a7d2787cca5b4f713184edc12d81cde283ddc4199067bb440094727a89ddad8d","target":"graph","created_at":"2026-05-18T02:21:34Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"Many popular statistical models, such as factor and random effects models, give arise a certain type of covariance structures that is a summation of low rank and sparse matrices. This paper introduces a penalized approximation framework to recover such model structures from large covariance matrix estimation. We propose an estimator based on minimizing a non-likelihood loss with separable non-smooth penalty functions. This estimator is shown to recover exactly the rank and sparsity patterns of these two components, and thus partially recovers the model structures. Convergence rates under vario","authors_text":"Xi Luo","cross_cats":["q-fin.PM","q-fin.ST"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2011-11-04T14:12:49Z","title":"Recovering Model Structures from Large Low Rank and Sparse Covariance Matrix Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1111.1133","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:89f58182ec77de14d37789662ea48124ec0b69a6200436d89447259f543ebb1f","target":"record","created_at":"2026-05-18T02:21:34Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"8f45c7db41ea3a198078b1d55d26ab4a123549d565086eb9cdda03df20f859cc","cross_cats_sorted":["q-fin.PM","q-fin.ST"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2011-11-04T14:12:49Z","title_canon_sha256":"d9134888069dec2b11fa8022a7ecceedad10d622bdc89592ffb1ac19abe27227"},"schema_version":"1.0","source":{"id":"1111.1133","kind":"arxiv","version":2}},"canonical_sha256":"17ed673045b0e995b539976af0bbb1c666cbb21f100ff19e295e10582a595fe9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"17ed673045b0e995b539976af0bbb1c666cbb21f100ff19e295e10582a595fe9","first_computed_at":"2026-05-18T02:21:34.908599Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:21:34.908599Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HHtyCwYCmSK2U70denLoyaqkh/cFCDTsLprJgi7fkdTlzONNoo9fGtNmLN6lmZKRQVHNoN0NAVuYEsVWvUhDBA==","signature_status":"signed_v1","signed_at":"2026-05-18T02:21:34.909043Z","signed_message":"canonical_sha256_bytes"},"source_id":"1111.1133","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:89f58182ec77de14d37789662ea48124ec0b69a6200436d89447259f543ebb1f","sha256:a7d2787cca5b4f713184edc12d81cde283ddc4199067bb440094727a89ddad8d"],"state_sha256":"25b6cf0bd4e1fd649d8f6c7eb1eb7aa95a35a717f3efcbb5844a13657d389d07"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"kJtRPBgarRVlc8g9q2XfqvKB6CiP8BrGBW3G6SNIdVQKVNC5tbRhpgL2I3+AqGxeYw0relPcgPBMGURI9iBVBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-18T22:33:21.639229Z","bundle_sha256":"a88cbcdd6c140c0a610e6e18e5141c2f5092d1323d3d6ac6ab6c3f2efc0f0ccb"}}