{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:EJJ2UW5T7EZEKIBVDNC7EMF6XT","short_pith_number":"pith:EJJ2UW5T","schema_version":"1.0","canonical_sha256":"2253aa5bb3f9324520351b45f230bebcd057efb7496ae1aaf7a118f4ae85e567","source":{"kind":"arxiv","id":"2504.03916","version":1},"attestation_state":"computed","paper":{"title":"Common Drivers in Sparsely Interacting Hawkes Processes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Alexander Kreiss, Enno Mammen, Wolfgang Polonik","submitted_at":"2025-04-04T20:27:55Z","abstract_excerpt":"We study a multivariate Hawkes process as a model for time-continuous relational event networks. The model does not assume the network to be known, it includes covariates, and it allows for both common drivers, parameters common to all the actors in the network, and also local parameters specific for each actor. We derive rates of convergence for all of the model parameters when both the number of actors and the time horizon tends to infinity. To prevent an exploding network, sparseness is assumed. We also discuss numerical aspects."},"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":"2504.03916","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2025-04-04T20:27:55Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"de2b3cf880272a87388da46d8ebeabd987f879061c7436d0b10769cfd8682ffa","abstract_canon_sha256":"2a16d1fb40675e03222fba808640fcbea4208b5683302432952197733f3bef93"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:44:51.068550Z","signature_b64":"TMa6cDQAheywo6mBoTKjnwrqtPm/RqUmkAGDF7pdjiq9n3AmfE9CPjMjqqz2Uf0EXxD2EE4pPg684Iml8mXaDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2253aa5bb3f9324520351b45f230bebcd057efb7496ae1aaf7a118f4ae85e567","last_reissued_at":"2026-07-05T10:44:51.068054Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:44:51.068054Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Common Drivers in Sparsely Interacting Hawkes Processes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Alexander Kreiss, Enno Mammen, Wolfgang Polonik","submitted_at":"2025-04-04T20:27:55Z","abstract_excerpt":"We study a multivariate Hawkes process as a model for time-continuous relational event networks. The model does not assume the network to be known, it includes covariates, and it allows for both common drivers, parameters common to all the actors in the network, and also local parameters specific for each actor. We derive rates of convergence for all of the model parameters when both the number of actors and the time horizon tends to infinity. To prevent an exploding network, sparseness is assumed. We also discuss numerical aspects."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.03916","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/2504.03916/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":"2504.03916","created_at":"2026-07-05T10:44:51.068113+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.03916v1","created_at":"2026-07-05T10:44:51.068113+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.03916","created_at":"2026-07-05T10:44:51.068113+00:00"},{"alias_kind":"pith_short_12","alias_value":"EJJ2UW5T7EZE","created_at":"2026-07-05T10:44:51.068113+00:00"},{"alias_kind":"pith_short_16","alias_value":"EJJ2UW5T7EZEKIBV","created_at":"2026-07-05T10:44:51.068113+00:00"},{"alias_kind":"pith_short_8","alias_value":"EJJ2UW5T","created_at":"2026-07-05T10:44:51.068113+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2511.10132","citing_title":"Hawkes autoregressive processes: a new model for multiscale and heterogeneous processes","ref_index":36,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/EJJ2UW5T7EZEKIBVDNC7EMF6XT","json":"https://pith.science/pith/EJJ2UW5T7EZEKIBVDNC7EMF6XT.json","graph_json":"https://pith.science/api/pith-number/EJJ2UW5T7EZEKIBVDNC7EMF6XT/graph.json","events_json":"https://pith.science/api/pith-number/EJJ2UW5T7EZEKIBVDNC7EMF6XT/events.json","paper":"https://pith.science/paper/EJJ2UW5T"},"agent_actions":{"view_html":"https://pith.science/pith/EJJ2UW5T7EZEKIBVDNC7EMF6XT","download_json":"https://pith.science/pith/EJJ2UW5T7EZEKIBVDNC7EMF6XT.json","view_paper":"https://pith.science/paper/EJJ2UW5T","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.03916&json=true","fetch_graph":"https://pith.science/api/pith-number/EJJ2UW5T7EZEKIBVDNC7EMF6XT/graph.json","fetch_events":"https://pith.science/api/pith-number/EJJ2UW5T7EZEKIBVDNC7EMF6XT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EJJ2UW5T7EZEKIBVDNC7EMF6XT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EJJ2UW5T7EZEKIBVDNC7EMF6XT/action/storage_attestation","attest_author":"https://pith.science/pith/EJJ2UW5T7EZEKIBVDNC7EMF6XT/action/author_attestation","sign_citation":"https://pith.science/pith/EJJ2UW5T7EZEKIBVDNC7EMF6XT/action/citation_signature","submit_replication":"https://pith.science/pith/EJJ2UW5T7EZEKIBVDNC7EMF6XT/action/replication_record"}},"created_at":"2026-07-05T10:44:51.068113+00:00","updated_at":"2026-07-05T10:44:51.068113+00:00"}