{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:4RHUUR3TOSLV4MDHK6FJBEYVPF","short_pith_number":"pith:4RHUUR3T","schema_version":"1.0","canonical_sha256":"e44f4a477374975e3067578a90931579422cb633520d0b696c64fb3d724c083a","source":{"kind":"arxiv","id":"1311.0219","version":2},"attestation_state":"computed","paper":{"title":"Joint Estimation of Multiple Graphical Models from High Dimensional Time Series","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Brian Caffo, Fang Han, Han Liu, Huitong Qiu","submitted_at":"2013-11-01T16:32:17Z","abstract_excerpt":"In this manuscript we consider the problem of jointly estimating multiple graphical models in high dimensions. We assume that the data are collected from n subjects, each of which consists of T possibly dependent observations. The graphical models of subjects vary, but are assumed to change smoothly corresponding to a measure of closeness between subjects. We propose a kernel based method for jointly estimating all graphical models. Theoretically, under a double asymptotic framework, where both (T,n) and the dimension d can increase, we provide the explicit rate of convergence in parameter est"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1311.0219","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-11-01T16:32:17Z","cross_cats_sorted":[],"title_canon_sha256":"e528ec488e8269f8ea4993c99c47c8279b7ab8ee29b138c098da36f8419f87e4","abstract_canon_sha256":"75f5adf462a559eec944db2ba4067c47bc04d92d5bc140ea9d75275666891110"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:40:54.166726Z","signature_b64":"t8bmz68kpDBc9c9XEL7p9scNIvBaXP1Pr0+Nty4DmiMnu1j8+2qaTczf/hoKRZC2Q3Y+B5dB286u3KkmMwkSCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e44f4a477374975e3067578a90931579422cb633520d0b696c64fb3d724c083a","last_reissued_at":"2026-05-18T02:40:54.166164Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:40:54.166164Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Joint Estimation of Multiple Graphical Models from High Dimensional Time Series","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ML","authors_text":"Brian Caffo, Fang Han, Han Liu, Huitong Qiu","submitted_at":"2013-11-01T16:32:17Z","abstract_excerpt":"In this manuscript we consider the problem of jointly estimating multiple graphical models in high dimensions. We assume that the data are collected from n subjects, each of which consists of T possibly dependent observations. The graphical models of subjects vary, but are assumed to change smoothly corresponding to a measure of closeness between subjects. We propose a kernel based method for jointly estimating all graphical models. Theoretically, under a double asymptotic framework, where both (T,n) and the dimension d can increase, we provide the explicit rate of convergence in parameter est"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1311.0219","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1311.0219","created_at":"2026-05-18T02:40:54.166233+00:00"},{"alias_kind":"arxiv_version","alias_value":"1311.0219v2","created_at":"2026-05-18T02:40:54.166233+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1311.0219","created_at":"2026-05-18T02:40:54.166233+00:00"},{"alias_kind":"pith_short_12","alias_value":"4RHUUR3TOSLV","created_at":"2026-05-18T12:27:34.582898+00:00"},{"alias_kind":"pith_short_16","alias_value":"4RHUUR3TOSLV4MDH","created_at":"2026-05-18T12:27:34.582898+00:00"},{"alias_kind":"pith_short_8","alias_value":"4RHUUR3T","created_at":"2026-05-18T12:27:34.582898+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/4RHUUR3TOSLV4MDHK6FJBEYVPF","json":"https://pith.science/pith/4RHUUR3TOSLV4MDHK6FJBEYVPF.json","graph_json":"https://pith.science/api/pith-number/4RHUUR3TOSLV4MDHK6FJBEYVPF/graph.json","events_json":"https://pith.science/api/pith-number/4RHUUR3TOSLV4MDHK6FJBEYVPF/events.json","paper":"https://pith.science/paper/4RHUUR3T"},"agent_actions":{"view_html":"https://pith.science/pith/4RHUUR3TOSLV4MDHK6FJBEYVPF","download_json":"https://pith.science/pith/4RHUUR3TOSLV4MDHK6FJBEYVPF.json","view_paper":"https://pith.science/paper/4RHUUR3T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1311.0219&json=true","fetch_graph":"https://pith.science/api/pith-number/4RHUUR3TOSLV4MDHK6FJBEYVPF/graph.json","fetch_events":"https://pith.science/api/pith-number/4RHUUR3TOSLV4MDHK6FJBEYVPF/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4RHUUR3TOSLV4MDHK6FJBEYVPF/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4RHUUR3TOSLV4MDHK6FJBEYVPF/action/storage_attestation","attest_author":"https://pith.science/pith/4RHUUR3TOSLV4MDHK6FJBEYVPF/action/author_attestation","sign_citation":"https://pith.science/pith/4RHUUR3TOSLV4MDHK6FJBEYVPF/action/citation_signature","submit_replication":"https://pith.science/pith/4RHUUR3TOSLV4MDHK6FJBEYVPF/action/replication_record"}},"created_at":"2026-05-18T02:40:54.166233+00:00","updated_at":"2026-05-18T02:40:54.166233+00:00"}