{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:ZIIGTVXGIAWKB24XT2H4FK25NU","short_pith_number":"pith:ZIIGTVXG","schema_version":"1.0","canonical_sha256":"ca1069d6e6402ca0eb979e8fc2ab5d6d3655fda9d42a9b59932f6f7de56453cc","source":{"kind":"arxiv","id":"2511.17291","version":2},"attestation_state":"computed","paper":{"title":"Properties of stepwise parameter estimation in high-dimensional vine copulas","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Jana Gauss, Thomas Nagler","submitted_at":"2025-11-21T15:03:12Z","abstract_excerpt":"The increasing use of vine copulas in high-dimensional settings, where the number of parameters is often of the same order as the sample size, calls for asymptotic theory beyond the traditional fixed-$p$, large-$n$ framework. We establish consistency and asymptotic normality of the stepwise maximum likelihood estimator for vine copulas when the number of parameters diverges as $n \\to \\infty$. Our theoretical results cover both parametric and nonparametric estimation of the marginal distributions, as well as truncated vines, and are also applicable to general estimation problems, particularly o"},"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":"2511.17291","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2025-11-21T15:03:12Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"264b376e0d96d1fbf874bdb8ab85c8c3c9c2ead57650e0c93dc7e247b9b827af","abstract_canon_sha256":"168fbb886b10f40ad58d856ccef07232d23dfaacc7c1524bce6ade7ed53b83bb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-28T01:05:11.015468Z","signature_b64":"5B75rwE0/BM9AVbntt+O/3n88T8onPdrrqtgcJzG0ZIGajkIxjB6Mqb6x0QWu/ozUaV+Udn/dPi/oDiov7KZDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ca1069d6e6402ca0eb979e8fc2ab5d6d3655fda9d42a9b59932f6f7de56453cc","last_reissued_at":"2026-05-28T01:05:11.014855Z","signature_status":"signed_v1","first_computed_at":"2026-05-28T01:05:11.014855Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Properties of stepwise parameter estimation in high-dimensional vine copulas","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Jana Gauss, Thomas Nagler","submitted_at":"2025-11-21T15:03:12Z","abstract_excerpt":"The increasing use of vine copulas in high-dimensional settings, where the number of parameters is often of the same order as the sample size, calls for asymptotic theory beyond the traditional fixed-$p$, large-$n$ framework. We establish consistency and asymptotic normality of the stepwise maximum likelihood estimator for vine copulas when the number of parameters diverges as $n \\to \\infty$. Our theoretical results cover both parametric and nonparametric estimation of the marginal distributions, as well as truncated vines, and are also applicable to general estimation problems, particularly o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2511.17291","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2511.17291/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":"2511.17291","created_at":"2026-05-28T01:05:11.014953+00:00"},{"alias_kind":"arxiv_version","alias_value":"2511.17291v2","created_at":"2026-05-28T01:05:11.014953+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2511.17291","created_at":"2026-05-28T01:05:11.014953+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZIIGTVXGIAWK","created_at":"2026-05-28T01:05:11.014953+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZIIGTVXGIAWKB24X","created_at":"2026-05-28T01:05:11.014953+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZIIGTVXG","created_at":"2026-05-28T01:05:11.014953+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/ZIIGTVXGIAWKB24XT2H4FK25NU","json":"https://pith.science/pith/ZIIGTVXGIAWKB24XT2H4FK25NU.json","graph_json":"https://pith.science/api/pith-number/ZIIGTVXGIAWKB24XT2H4FK25NU/graph.json","events_json":"https://pith.science/api/pith-number/ZIIGTVXGIAWKB24XT2H4FK25NU/events.json","paper":"https://pith.science/paper/ZIIGTVXG"},"agent_actions":{"view_html":"https://pith.science/pith/ZIIGTVXGIAWKB24XT2H4FK25NU","download_json":"https://pith.science/pith/ZIIGTVXGIAWKB24XT2H4FK25NU.json","view_paper":"https://pith.science/paper/ZIIGTVXG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2511.17291&json=true","fetch_graph":"https://pith.science/api/pith-number/ZIIGTVXGIAWKB24XT2H4FK25NU/graph.json","fetch_events":"https://pith.science/api/pith-number/ZIIGTVXGIAWKB24XT2H4FK25NU/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZIIGTVXGIAWKB24XT2H4FK25NU/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZIIGTVXGIAWKB24XT2H4FK25NU/action/storage_attestation","attest_author":"https://pith.science/pith/ZIIGTVXGIAWKB24XT2H4FK25NU/action/author_attestation","sign_citation":"https://pith.science/pith/ZIIGTVXGIAWKB24XT2H4FK25NU/action/citation_signature","submit_replication":"https://pith.science/pith/ZIIGTVXGIAWKB24XT2H4FK25NU/action/replication_record"}},"created_at":"2026-05-28T01:05:11.014953+00:00","updated_at":"2026-05-28T01:05:11.014953+00:00"}