{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:ODGKMAF6NJZZIHJ6ZXJULDUTS2","short_pith_number":"pith:ODGKMAF6","schema_version":"1.0","canonical_sha256":"70cca600be6a73941d3ecdd3458e9396b7d57a9095a7771314c568d4cda9c6d6","source":{"kind":"arxiv","id":"2302.02768","version":1},"attestation_state":"computed","paper":{"title":"Network Autoregression for Incomplete Matrix-Valued Time Series","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Feifei Wang, Xuening Zhu, Yanyuan Ma, Zeng Li","submitted_at":"2023-02-06T13:26:25Z","abstract_excerpt":"We study the dynamics of matrix-valued time series with observed network structures by proposing a matrix network autoregression model with row and column networks of the subjects. We incorporate covariate information and a low rank intercept matrix. We allow incomplete observations in the matrices and the missing mechanism can be covariate dependent. To estimate the model, a two-step estimation procedure is proposed. The first step aims to estimate the network autoregression coefficients, and the second step aims to estimate the regression parameters, which are matrices themselves. Theoretica"},"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":"2302.02768","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2023-02-06T13:26:25Z","cross_cats_sorted":[],"title_canon_sha256":"e4caea3a8ba1cb4c48cd05c66d9cbdeb80b027610e39ffb463735d76e2a359eb","abstract_canon_sha256":"7011e862f52e29346bfa5066502a1386d9044b8208251515e8d59996cb2986c1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:39:06.624909Z","signature_b64":"Appes/Aco4YAgl5dV7KZqk4bIz/9wI92nmUZh3DdaIgLcDr462kFvaIxYunuxX/GHT5ceEbASpifLknFLlGODQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"70cca600be6a73941d3ecdd3458e9396b7d57a9095a7771314c568d4cda9c6d6","last_reissued_at":"2026-07-05T05:39:06.624547Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:39:06.624547Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Network Autoregression for Incomplete Matrix-Valued Time Series","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Feifei Wang, Xuening Zhu, Yanyuan Ma, Zeng Li","submitted_at":"2023-02-06T13:26:25Z","abstract_excerpt":"We study the dynamics of matrix-valued time series with observed network structures by proposing a matrix network autoregression model with row and column networks of the subjects. We incorporate covariate information and a low rank intercept matrix. We allow incomplete observations in the matrices and the missing mechanism can be covariate dependent. To estimate the model, a two-step estimation procedure is proposed. The first step aims to estimate the network autoregression coefficients, and the second step aims to estimate the regression parameters, which are matrices themselves. Theoretica"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2302.02768","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2302.02768/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2302.02768","created_at":"2026-07-05T05:39:06.624602+00:00"},{"alias_kind":"arxiv_version","alias_value":"2302.02768v1","created_at":"2026-07-05T05:39:06.624602+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2302.02768","created_at":"2026-07-05T05:39:06.624602+00:00"},{"alias_kind":"pith_short_12","alias_value":"ODGKMAF6NJZZ","created_at":"2026-07-05T05:39:06.624602+00:00"},{"alias_kind":"pith_short_16","alias_value":"ODGKMAF6NJZZIHJ6","created_at":"2026-07-05T05:39:06.624602+00:00"},{"alias_kind":"pith_short_8","alias_value":"ODGKMAF6","created_at":"2026-07-05T05:39:06.624602+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/ODGKMAF6NJZZIHJ6ZXJULDUTS2","json":"https://pith.science/pith/ODGKMAF6NJZZIHJ6ZXJULDUTS2.json","graph_json":"https://pith.science/api/pith-number/ODGKMAF6NJZZIHJ6ZXJULDUTS2/graph.json","events_json":"https://pith.science/api/pith-number/ODGKMAF6NJZZIHJ6ZXJULDUTS2/events.json","paper":"https://pith.science/paper/ODGKMAF6"},"agent_actions":{"view_html":"https://pith.science/pith/ODGKMAF6NJZZIHJ6ZXJULDUTS2","download_json":"https://pith.science/pith/ODGKMAF6NJZZIHJ6ZXJULDUTS2.json","view_paper":"https://pith.science/paper/ODGKMAF6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2302.02768&json=true","fetch_graph":"https://pith.science/api/pith-number/ODGKMAF6NJZZIHJ6ZXJULDUTS2/graph.json","fetch_events":"https://pith.science/api/pith-number/ODGKMAF6NJZZIHJ6ZXJULDUTS2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ODGKMAF6NJZZIHJ6ZXJULDUTS2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ODGKMAF6NJZZIHJ6ZXJULDUTS2/action/storage_attestation","attest_author":"https://pith.science/pith/ODGKMAF6NJZZIHJ6ZXJULDUTS2/action/author_attestation","sign_citation":"https://pith.science/pith/ODGKMAF6NJZZIHJ6ZXJULDUTS2/action/citation_signature","submit_replication":"https://pith.science/pith/ODGKMAF6NJZZIHJ6ZXJULDUTS2/action/replication_record"}},"created_at":"2026-07-05T05:39:06.624602+00:00","updated_at":"2026-07-05T05:39:06.624602+00:00"}