{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2014:2AWP6QBKRYCFNGEZB3NWBGJFMA","short_pith_number":"pith:2AWP6QBK","schema_version":"1.0","canonical_sha256":"d02cff402a8e045698990edb6099256021dbf052294b0dbebc9654caf91be8ff","source":{"kind":"arxiv","id":"1403.2332","version":8},"attestation_state":"computed","paper":{"title":"A Mixture of Coalesced Generalized Hyperbolic Distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Brian C. Franczak, Cristina Tortora, Paul D. McNicholas, Ryan P. Browne","submitted_at":"2014-03-10T18:25:17Z","abstract_excerpt":"A mixture of multiple scaled generalized hyperbolic distributions (MMSGHDs) is introduced. Then, a coalesced generalized hyperbolic distribution (CGHD) is developed by joining a generalized hyperbolic distribution with a multiple scaled generalized hyperbolic distribution. After detailing the development of the MMSGHDs, which arises via implementation of a multi-dimensional weight function, the density of the mixture of CGHDs is developed. A parameter estimation scheme is developed using the ever-expanding class of MM algorithms and the Bayesian information criterion is used for model selectio"},"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":"1403.2332","kind":"arxiv","version":8},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2014-03-10T18:25:17Z","cross_cats_sorted":[],"title_canon_sha256":"1ff957baf849ce67a032ab82792166a7ccc3ad58b61c36c23b8d3b3c8f5afe47","abstract_canon_sha256":"90d6f1c0db5418a8a0ab7d34a245b9a69fa27802037b406914dc92a57e9d11b1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:14.073596Z","signature_b64":"rgwqHZUTOs9hG1HmGb6Q/4gzhaxCdpwWpfsAglamjHToSF9cpIMkIj/VGK75LL089QkIDyYjN0e/O19LgCZTDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d02cff402a8e045698990edb6099256021dbf052294b0dbebc9654caf91be8ff","last_reissued_at":"2026-05-18T00:02:14.073092Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:14.073092Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Mixture of Coalesced Generalized Hyperbolic Distributions","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Brian C. Franczak, Cristina Tortora, Paul D. McNicholas, Ryan P. Browne","submitted_at":"2014-03-10T18:25:17Z","abstract_excerpt":"A mixture of multiple scaled generalized hyperbolic distributions (MMSGHDs) is introduced. Then, a coalesced generalized hyperbolic distribution (CGHD) is developed by joining a generalized hyperbolic distribution with a multiple scaled generalized hyperbolic distribution. After detailing the development of the MMSGHDs, which arises via implementation of a multi-dimensional weight function, the density of the mixture of CGHDs is developed. A parameter estimation scheme is developed using the ever-expanding class of MM algorithms and the Bayesian information criterion is used for model selectio"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1403.2332","kind":"arxiv","version":8},"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":"1403.2332","created_at":"2026-05-18T00:02:14.073172+00:00"},{"alias_kind":"arxiv_version","alias_value":"1403.2332v8","created_at":"2026-05-18T00:02:14.073172+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1403.2332","created_at":"2026-05-18T00:02:14.073172+00:00"},{"alias_kind":"pith_short_12","alias_value":"2AWP6QBKRYCF","created_at":"2026-05-18T12:28:09.283467+00:00"},{"alias_kind":"pith_short_16","alias_value":"2AWP6QBKRYCFNGEZ","created_at":"2026-05-18T12:28:09.283467+00:00"},{"alias_kind":"pith_short_8","alias_value":"2AWP6QBK","created_at":"2026-05-18T12:28:09.283467+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/2AWP6QBKRYCFNGEZB3NWBGJFMA","json":"https://pith.science/pith/2AWP6QBKRYCFNGEZB3NWBGJFMA.json","graph_json":"https://pith.science/api/pith-number/2AWP6QBKRYCFNGEZB3NWBGJFMA/graph.json","events_json":"https://pith.science/api/pith-number/2AWP6QBKRYCFNGEZB3NWBGJFMA/events.json","paper":"https://pith.science/paper/2AWP6QBK"},"agent_actions":{"view_html":"https://pith.science/pith/2AWP6QBKRYCFNGEZB3NWBGJFMA","download_json":"https://pith.science/pith/2AWP6QBKRYCFNGEZB3NWBGJFMA.json","view_paper":"https://pith.science/paper/2AWP6QBK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1403.2332&json=true","fetch_graph":"https://pith.science/api/pith-number/2AWP6QBKRYCFNGEZB3NWBGJFMA/graph.json","fetch_events":"https://pith.science/api/pith-number/2AWP6QBKRYCFNGEZB3NWBGJFMA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2AWP6QBKRYCFNGEZB3NWBGJFMA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2AWP6QBKRYCFNGEZB3NWBGJFMA/action/storage_attestation","attest_author":"https://pith.science/pith/2AWP6QBKRYCFNGEZB3NWBGJFMA/action/author_attestation","sign_citation":"https://pith.science/pith/2AWP6QBKRYCFNGEZB3NWBGJFMA/action/citation_signature","submit_replication":"https://pith.science/pith/2AWP6QBKRYCFNGEZB3NWBGJFMA/action/replication_record"}},"created_at":"2026-05-18T00:02:14.073172+00:00","updated_at":"2026-05-18T00:02:14.073172+00:00"}