{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:DFLAVSQX7UXWCRGGIC2G77VELG","short_pith_number":"pith:DFLAVSQX","canonical_record":{"source":{"id":"1208.4275","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2012-08-21T14:20:05Z","cross_cats_sorted":[],"title_canon_sha256":"fe27b563ad690fe4d6bed68e3d0096027ba79b2720ba585ac89ad6510feb5e56","abstract_canon_sha256":"0d49e892610054cfdd28858224ffc621e79e966b8c13a7d48ed9e895841a9915"},"schema_version":"1.0"},"canonical_sha256":"19560aca17fd2f6144c640b46ffea459a243acd20c0b09d9f60e1b992d03b528","source":{"kind":"arxiv","id":"1208.4275","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1208.4275","created_at":"2026-05-18T03:48:23Z"},{"alias_kind":"arxiv_version","alias_value":"1208.4275v1","created_at":"2026-05-18T03:48:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1208.4275","created_at":"2026-05-18T03:48:23Z"},{"alias_kind":"pith_short_12","alias_value":"DFLAVSQX7UXW","created_at":"2026-05-18T12:27:04Z"},{"alias_kind":"pith_short_16","alias_value":"DFLAVSQX7UXWCRGG","created_at":"2026-05-18T12:27:04Z"},{"alias_kind":"pith_short_8","alias_value":"DFLAVSQX","created_at":"2026-05-18T12:27:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:DFLAVSQX7UXWCRGGIC2G77VELG","target":"record","payload":{"canonical_record":{"source":{"id":"1208.4275","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2012-08-21T14:20:05Z","cross_cats_sorted":[],"title_canon_sha256":"fe27b563ad690fe4d6bed68e3d0096027ba79b2720ba585ac89ad6510feb5e56","abstract_canon_sha256":"0d49e892610054cfdd28858224ffc621e79e966b8c13a7d48ed9e895841a9915"},"schema_version":"1.0"},"canonical_sha256":"19560aca17fd2f6144c640b46ffea459a243acd20c0b09d9f60e1b992d03b528","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:48:23.991847Z","signature_b64":"5NqXjKO9NkddScNbaLJFkb4Ky4YIJarkNQIMLi+CWxaSo1yvLDLRLM2sssydI1pWm+JfQ7Ycx7GGvyqyTyg+Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"19560aca17fd2f6144c640b46ffea459a243acd20c0b09d9f60e1b992d03b528","last_reissued_at":"2026-05-18T03:48:23.991355Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:48:23.991355Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1208.4275","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T03:48:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HzHccoBjp3xjYWOcrG2aFP/GnjcX8OrHlgbuF9F5wDRIcOtx7ErjbI6CM2A79QNfhrl4XSjL56bpv7wa3geTBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T17:25:00.541600Z"},"content_sha256":"da92b3ecb5cbd9cf29edde7a2af84a3a85754b1b713a58c8b3a8211bf03972ff","schema_version":"1.0","event_id":"sha256:da92b3ecb5cbd9cf29edde7a2af84a3a85754b1b713a58c8b3a8211bf03972ff"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:DFLAVSQX7UXWCRGGIC2G77VELG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Composite likelihood estimation of sparse Gaussian graphical models with symmetry","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Helene Massam, Xin Gao","submitted_at":"2012-08-21T14:20:05Z","abstract_excerpt":"In this article, we discuss the composite likelihood estimation of sparse Gaussian graphical models. When there are symmetry constraints on the concentration matrix or partial correlation matrix, the likelihood estimation can be computational intensive. The composite likelihood offers an alternative formulation of the objective function and yields consistent estimators. When a sparse model is considered, the penalized composite likelihood estimation can yield estimates satisfying both the symmetry and sparsity constraints and possess ORACLE property. Application of the proposed method is demon"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1208.4275","kind":"arxiv","version":1},"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":"2026-05-18T03:48:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v0Cr1GyuK2IvX+EEc1xAMg5u4+Ieirw1duQkiqUBBGUts8J3Yg0GGUdBOET/e5QbmPchm7hHq6Tv6ImZHibcAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-23T17:25:00.542213Z"},"content_sha256":"893a22e3ea016d76d642dd014f84db9f1dfad2d446623ebf887ab54c80905e99","schema_version":"1.0","event_id":"sha256:893a22e3ea016d76d642dd014f84db9f1dfad2d446623ebf887ab54c80905e99"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DFLAVSQX7UXWCRGGIC2G77VELG/bundle.json","state_url":"https://pith.science/pith/DFLAVSQX7UXWCRGGIC2G77VELG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DFLAVSQX7UXWCRGGIC2G77VELG/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":"2026-05-23T17:25:00Z","links":{"resolver":"https://pith.science/pith/DFLAVSQX7UXWCRGGIC2G77VELG","bundle":"https://pith.science/pith/DFLAVSQX7UXWCRGGIC2G77VELG/bundle.json","state":"https://pith.science/pith/DFLAVSQX7UXWCRGGIC2G77VELG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DFLAVSQX7UXWCRGGIC2G77VELG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:DFLAVSQX7UXWCRGGIC2G77VELG","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":"0d49e892610054cfdd28858224ffc621e79e966b8c13a7d48ed9e895841a9915","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2012-08-21T14:20:05Z","title_canon_sha256":"fe27b563ad690fe4d6bed68e3d0096027ba79b2720ba585ac89ad6510feb5e56"},"schema_version":"1.0","source":{"id":"1208.4275","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1208.4275","created_at":"2026-05-18T03:48:23Z"},{"alias_kind":"arxiv_version","alias_value":"1208.4275v1","created_at":"2026-05-18T03:48:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1208.4275","created_at":"2026-05-18T03:48:23Z"},{"alias_kind":"pith_short_12","alias_value":"DFLAVSQX7UXW","created_at":"2026-05-18T12:27:04Z"},{"alias_kind":"pith_short_16","alias_value":"DFLAVSQX7UXWCRGG","created_at":"2026-05-18T12:27:04Z"},{"alias_kind":"pith_short_8","alias_value":"DFLAVSQX","created_at":"2026-05-18T12:27:04Z"}],"graph_snapshots":[{"event_id":"sha256:893a22e3ea016d76d642dd014f84db9f1dfad2d446623ebf887ab54c80905e99","target":"graph","created_at":"2026-05-18T03:48:23Z","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":"In this article, we discuss the composite likelihood estimation of sparse Gaussian graphical models. When there are symmetry constraints on the concentration matrix or partial correlation matrix, the likelihood estimation can be computational intensive. The composite likelihood offers an alternative formulation of the objective function and yields consistent estimators. When a sparse model is considered, the penalized composite likelihood estimation can yield estimates satisfying both the symmetry and sparsity constraints and possess ORACLE property. Application of the proposed method is demon","authors_text":"Helene Massam, Xin Gao","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2012-08-21T14:20:05Z","title":"Composite likelihood estimation of sparse Gaussian graphical models with symmetry"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1208.4275","kind":"arxiv","version":1},"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:da92b3ecb5cbd9cf29edde7a2af84a3a85754b1b713a58c8b3a8211bf03972ff","target":"record","created_at":"2026-05-18T03:48:23Z","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":"0d49e892610054cfdd28858224ffc621e79e966b8c13a7d48ed9e895841a9915","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2012-08-21T14:20:05Z","title_canon_sha256":"fe27b563ad690fe4d6bed68e3d0096027ba79b2720ba585ac89ad6510feb5e56"},"schema_version":"1.0","source":{"id":"1208.4275","kind":"arxiv","version":1}},"canonical_sha256":"19560aca17fd2f6144c640b46ffea459a243acd20c0b09d9f60e1b992d03b528","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"19560aca17fd2f6144c640b46ffea459a243acd20c0b09d9f60e1b992d03b528","first_computed_at":"2026-05-18T03:48:23.991355Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:48:23.991355Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5NqXjKO9NkddScNbaLJFkb4Ky4YIJarkNQIMLi+CWxaSo1yvLDLRLM2sssydI1pWm+JfQ7Ycx7GGvyqyTyg+Dw==","signature_status":"signed_v1","signed_at":"2026-05-18T03:48:23.991847Z","signed_message":"canonical_sha256_bytes"},"source_id":"1208.4275","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:da92b3ecb5cbd9cf29edde7a2af84a3a85754b1b713a58c8b3a8211bf03972ff","sha256:893a22e3ea016d76d642dd014f84db9f1dfad2d446623ebf887ab54c80905e99"],"state_sha256":"17b76401dadd5a8b604149a5bcff7e342f32b5e4fa0bc5dec3ad2afd1430eff2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bd4TrA+FZ6+GsX53xvYPfcb3ff+Ltbak65b2gu8Vsi2DBUvYS+dlxOjbdokXg40Bc/MYPMSf8fAl9tdLh5h8CQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-23T17:25:00.545415Z","bundle_sha256":"63bf03c36636712723b53458dec8019fb7c7f8fed01f38b28c14f97217c6fd08"}}