{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:QQHEH7QJ47L27UXK7Q2EBGUPX2","short_pith_number":"pith:QQHEH7QJ","schema_version":"1.0","canonical_sha256":"840e43fe09e7d7afd2eafc34409a8fbea53117e2ae60c9db4161a1a5348752dd","source":{"kind":"arxiv","id":"2402.03206","version":1},"attestation_state":"computed","paper":{"title":"Inverse regression for spatially distributed functional data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Ruiqi Liu, Suneel Babu Chatla","submitted_at":"2024-02-05T17:16:43Z","abstract_excerpt":"Spatially distributed functional data are prevalent in many statistical applications such as meteorology, energy forecasting, census data, disease mapping, and neurological studies. Given their complex and high-dimensional nature, functional data often require dimension reduction methods to extract meaningful information. Inverse regression is one such approach that has become very popular in the past two decades. We study the inverse regression in the framework of functional data observed at irregularly positioned spatial sites. The functional predictor is the sum of a spatially dependent fun"},"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":"2402.03206","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.ST","submitted_at":"2024-02-05T17:16:43Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"9d184a2fbf22e5d020190c18bf4fdfbcf2d497f9d4a3e8b61bbd0ad9f6dff63b","abstract_canon_sha256":"c5cdbbab1febe2a14327c90b2bb4c020400c1e644d593a1fa9c107ed1284e9f4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:41:32.404385Z","signature_b64":"iUX2W86clKnWmClYmqYdm7VKgJ5+2q6OEJajmoBmM2yyJprBfu2c7iR7scn3OEFfjblybVDB3DrPQxF8FI1ZCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"840e43fe09e7d7afd2eafc34409a8fbea53117e2ae60c9db4161a1a5348752dd","last_reissued_at":"2026-07-05T07:41:32.403836Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:41:32.403836Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Inverse regression for spatially distributed functional data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Ruiqi Liu, Suneel Babu Chatla","submitted_at":"2024-02-05T17:16:43Z","abstract_excerpt":"Spatially distributed functional data are prevalent in many statistical applications such as meteorology, energy forecasting, census data, disease mapping, and neurological studies. Given their complex and high-dimensional nature, functional data often require dimension reduction methods to extract meaningful information. Inverse regression is one such approach that has become very popular in the past two decades. We study the inverse regression in the framework of functional data observed at irregularly positioned spatial sites. The functional predictor is the sum of a spatially dependent fun"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.03206","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/2402.03206/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":"2402.03206","created_at":"2026-07-05T07:41:32.403927+00:00"},{"alias_kind":"arxiv_version","alias_value":"2402.03206v1","created_at":"2026-07-05T07:41:32.403927+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.03206","created_at":"2026-07-05T07:41:32.403927+00:00"},{"alias_kind":"pith_short_12","alias_value":"QQHEH7QJ47L2","created_at":"2026-07-05T07:41:32.403927+00:00"},{"alias_kind":"pith_short_16","alias_value":"QQHEH7QJ47L27UXK","created_at":"2026-07-05T07:41:32.403927+00:00"},{"alias_kind":"pith_short_8","alias_value":"QQHEH7QJ","created_at":"2026-07-05T07:41:32.403927+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/QQHEH7QJ47L27UXK7Q2EBGUPX2","json":"https://pith.science/pith/QQHEH7QJ47L27UXK7Q2EBGUPX2.json","graph_json":"https://pith.science/api/pith-number/QQHEH7QJ47L27UXK7Q2EBGUPX2/graph.json","events_json":"https://pith.science/api/pith-number/QQHEH7QJ47L27UXK7Q2EBGUPX2/events.json","paper":"https://pith.science/paper/QQHEH7QJ"},"agent_actions":{"view_html":"https://pith.science/pith/QQHEH7QJ47L27UXK7Q2EBGUPX2","download_json":"https://pith.science/pith/QQHEH7QJ47L27UXK7Q2EBGUPX2.json","view_paper":"https://pith.science/paper/QQHEH7QJ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2402.03206&json=true","fetch_graph":"https://pith.science/api/pith-number/QQHEH7QJ47L27UXK7Q2EBGUPX2/graph.json","fetch_events":"https://pith.science/api/pith-number/QQHEH7QJ47L27UXK7Q2EBGUPX2/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QQHEH7QJ47L27UXK7Q2EBGUPX2/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QQHEH7QJ47L27UXK7Q2EBGUPX2/action/storage_attestation","attest_author":"https://pith.science/pith/QQHEH7QJ47L27UXK7Q2EBGUPX2/action/author_attestation","sign_citation":"https://pith.science/pith/QQHEH7QJ47L27UXK7Q2EBGUPX2/action/citation_signature","submit_replication":"https://pith.science/pith/QQHEH7QJ47L27UXK7Q2EBGUPX2/action/replication_record"}},"created_at":"2026-07-05T07:41:32.403927+00:00","updated_at":"2026-07-05T07:41:32.403927+00:00"}