{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2012:Q52VQU5IIIKW2OXPINS3KY3DOY","short_pith_number":"pith:Q52VQU5I","canonical_record":{"source":{"id":"1203.1975","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-03-09T02:27:54Z","cross_cats_sorted":[],"title_canon_sha256":"8242d972f13eed583dcbc0768f1167ba41b00d3e8291fb8a641c961d8a0e3554","abstract_canon_sha256":"1d9614c919d74486bbabe09322562f0e129c82722e4899d1cc832d5831b60600"},"schema_version":"1.0"},"canonical_sha256":"87755853a842156d3aef4365b5636376171e665006a3c0173a34f89b9478851d","source":{"kind":"arxiv","id":"1203.1975","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1203.1975","created_at":"2026-05-18T02:53:54Z"},{"alias_kind":"arxiv_version","alias_value":"1203.1975v3","created_at":"2026-05-18T02:53:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1203.1975","created_at":"2026-05-18T02:53:54Z"},{"alias_kind":"pith_short_12","alias_value":"Q52VQU5IIIKW","created_at":"2026-05-18T12:27:18Z"},{"alias_kind":"pith_short_16","alias_value":"Q52VQU5IIIKW2OXP","created_at":"2026-05-18T12:27:18Z"},{"alias_kind":"pith_short_8","alias_value":"Q52VQU5I","created_at":"2026-05-18T12:27:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2012:Q52VQU5IIIKW2OXPINS3KY3DOY","target":"record","payload":{"canonical_record":{"source":{"id":"1203.1975","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-03-09T02:27:54Z","cross_cats_sorted":[],"title_canon_sha256":"8242d972f13eed583dcbc0768f1167ba41b00d3e8291fb8a641c961d8a0e3554","abstract_canon_sha256":"1d9614c919d74486bbabe09322562f0e129c82722e4899d1cc832d5831b60600"},"schema_version":"1.0"},"canonical_sha256":"87755853a842156d3aef4365b5636376171e665006a3c0173a34f89b9478851d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:53:54.283823Z","signature_b64":"P8wMVYTEdz0+gxNqz514WYzRvqlZweDKVRjGsLPEzPYS/xZ1Rr7BUvDlTnk2YoDsutgIrCTxs+1D8KjvV+eODQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"87755853a842156d3aef4365b5636376171e665006a3c0173a34f89b9478851d","last_reissued_at":"2026-05-18T02:53:54.283007Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:53:54.283007Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1203.1975","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:53:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"p73bwqMErUvMHxvfUjsxNz3VF5cMTMN0b7g6qojrQkVPkbOeHp9VaR2XSXkywPYIS2DwMGVEk1e8XN3+uvpdCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T08:40:08.446808Z"},"content_sha256":"1b0ba3be375e9c0e4a2de6f168e46b610eda6eb47ef14c739b89d014f841f53f","schema_version":"1.0","event_id":"sha256:1b0ba3be375e9c0e4a2de6f168e46b610eda6eb47ef14c739b89d014f841f53f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2012:Q52VQU5IIIKW2OXPINS3KY3DOY","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Warped Functional Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Daniel Gervini","submitted_at":"2012-03-09T02:27:54Z","abstract_excerpt":"A characteristic feature of functional data is the presence of phase variability in addition to amplitude variability. Existing functional regression methods do not handle time variability in an explicit and efficient way. In this paper we introduce a functional regression method that incorporates time warping as an intrinsic part of the model. The method achieves good predictive power in a parsimonious way and allows unified statistical inference about phase and amplitude components. The asymptotic distribution of the estimators is derived and the finite-sample properties are studied by simul"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1203.1975","kind":"arxiv","version":3},"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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T02:53:54Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HX35TF3Xyfz/qlv0xy2J+qNZ1fhdbVrMLZ3l1WHEmId+CYn7KMPDq//2ca6OoufhLsYjKwCShj1iIkc2FgB1AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T08:40:08.447371Z"},"content_sha256":"a7f90b3e4fd36f65a3570ee9a0a99d5b28ca31725f4caed141b083185760566b","schema_version":"1.0","event_id":"sha256:a7f90b3e4fd36f65a3570ee9a0a99d5b28ca31725f4caed141b083185760566b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/Q52VQU5IIIKW2OXPINS3KY3DOY/bundle.json","state_url":"https://pith.science/pith/Q52VQU5IIIKW2OXPINS3KY3DOY/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/Q52VQU5IIIKW2OXPINS3KY3DOY/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-07T08:40:08Z","links":{"resolver":"https://pith.science/pith/Q52VQU5IIIKW2OXPINS3KY3DOY","bundle":"https://pith.science/pith/Q52VQU5IIIKW2OXPINS3KY3DOY/bundle.json","state":"https://pith.science/pith/Q52VQU5IIIKW2OXPINS3KY3DOY/state.json","well_known_bundle":"https://pith.science/.well-known/pith/Q52VQU5IIIKW2OXPINS3KY3DOY/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2012:Q52VQU5IIIKW2OXPINS3KY3DOY","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"1d9614c919d74486bbabe09322562f0e129c82722e4899d1cc832d5831b60600","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-03-09T02:27:54Z","title_canon_sha256":"8242d972f13eed583dcbc0768f1167ba41b00d3e8291fb8a641c961d8a0e3554"},"schema_version":"1.0","source":{"id":"1203.1975","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1203.1975","created_at":"2026-05-18T02:53:54Z"},{"alias_kind":"arxiv_version","alias_value":"1203.1975v3","created_at":"2026-05-18T02:53:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1203.1975","created_at":"2026-05-18T02:53:54Z"},{"alias_kind":"pith_short_12","alias_value":"Q52VQU5IIIKW","created_at":"2026-05-18T12:27:18Z"},{"alias_kind":"pith_short_16","alias_value":"Q52VQU5IIIKW2OXP","created_at":"2026-05-18T12:27:18Z"},{"alias_kind":"pith_short_8","alias_value":"Q52VQU5I","created_at":"2026-05-18T12:27:18Z"}],"graph_snapshots":[{"event_id":"sha256:a7f90b3e4fd36f65a3570ee9a0a99d5b28ca31725f4caed141b083185760566b","target":"graph","created_at":"2026-05-18T02:53:54Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"paper":{"abstract_excerpt":"A characteristic feature of functional data is the presence of phase variability in addition to amplitude variability. Existing functional regression methods do not handle time variability in an explicit and efficient way. In this paper we introduce a functional regression method that incorporates time warping as an intrinsic part of the model. The method achieves good predictive power in a parsimonious way and allows unified statistical inference about phase and amplitude components. The asymptotic distribution of the estimators is derived and the finite-sample properties are studied by simul","authors_text":"Daniel Gervini","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-03-09T02:27:54Z","title":"Warped Functional Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1203.1975","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:1b0ba3be375e9c0e4a2de6f168e46b610eda6eb47ef14c739b89d014f841f53f","target":"record","created_at":"2026-05-18T02:53:54Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"1d9614c919d74486bbabe09322562f0e129c82722e4899d1cc832d5831b60600","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-03-09T02:27:54Z","title_canon_sha256":"8242d972f13eed583dcbc0768f1167ba41b00d3e8291fb8a641c961d8a0e3554"},"schema_version":"1.0","source":{"id":"1203.1975","kind":"arxiv","version":3}},"canonical_sha256":"87755853a842156d3aef4365b5636376171e665006a3c0173a34f89b9478851d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"87755853a842156d3aef4365b5636376171e665006a3c0173a34f89b9478851d","first_computed_at":"2026-05-18T02:53:54.283007Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:53:54.283007Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"P8wMVYTEdz0+gxNqz514WYzRvqlZweDKVRjGsLPEzPYS/xZ1Rr7BUvDlTnk2YoDsutgIrCTxs+1D8KjvV+eODQ==","signature_status":"signed_v1","signed_at":"2026-05-18T02:53:54.283823Z","signed_message":"canonical_sha256_bytes"},"source_id":"1203.1975","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1b0ba3be375e9c0e4a2de6f168e46b610eda6eb47ef14c739b89d014f841f53f","sha256:a7f90b3e4fd36f65a3570ee9a0a99d5b28ca31725f4caed141b083185760566b"],"state_sha256":"f5dfc6711db13d4461c4bab2b5e9f996553d3775c6779a8d3dd9707a9777f074"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"so4meEPDIxYts/f9IxV80929W06bEr/0seD8S/ZJH6s5c/ekLI3hNLce/7ovXaFdRs0bQFStQ7h4ltcSUlcMAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T08:40:08.450192Z","bundle_sha256":"ac6c6d0a8601d50e335a4288aae64f0632229c53d53c933b3b5175966f2df931"}}