{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:MHWH4XGN4QNUSMI7XUVBGRFOIL","short_pith_number":"pith:MHWH4XGN","canonical_record":{"source":{"id":"1909.08763","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-09-19T01:23:03Z","cross_cats_sorted":[],"title_canon_sha256":"26ccbe17ffad8c1ec8dcd611973b045e9681a491c0baba77e72863736a58a08f","abstract_canon_sha256":"aaf65146a0c7071ce3e29d1e07b42582a7a17a7b32a497e85505f9a393c8503f"},"schema_version":"1.0"},"canonical_sha256":"61ec7e5ccde41b49311fbd2a1344ae42eeb575f47cb4a0ccdd70483cdf046339","source":{"kind":"arxiv","id":"1909.08763","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.08763","created_at":"2026-07-05T00:05:37Z"},{"alias_kind":"arxiv_version","alias_value":"1909.08763v1","created_at":"2026-07-05T00:05:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.08763","created_at":"2026-07-05T00:05:37Z"},{"alias_kind":"pith_short_12","alias_value":"MHWH4XGN4QNU","created_at":"2026-07-05T00:05:37Z"},{"alias_kind":"pith_short_16","alias_value":"MHWH4XGN4QNUSMI7","created_at":"2026-07-05T00:05:37Z"},{"alias_kind":"pith_short_8","alias_value":"MHWH4XGN","created_at":"2026-07-05T00:05:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:MHWH4XGN4QNUSMI7XUVBGRFOIL","target":"record","payload":{"canonical_record":{"source":{"id":"1909.08763","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-09-19T01:23:03Z","cross_cats_sorted":[],"title_canon_sha256":"26ccbe17ffad8c1ec8dcd611973b045e9681a491c0baba77e72863736a58a08f","abstract_canon_sha256":"aaf65146a0c7071ce3e29d1e07b42582a7a17a7b32a497e85505f9a393c8503f"},"schema_version":"1.0"},"canonical_sha256":"61ec7e5ccde41b49311fbd2a1344ae42eeb575f47cb4a0ccdd70483cdf046339","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:05:37.478088Z","signature_b64":"1nN6X9yjJ5g37RmmUIgRoD8FobomqXMLn/PT3rPp3l91NPmmv5UDxG8rx1soHAupBGI2pG06ZajOEN0CuKU4Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"61ec7e5ccde41b49311fbd2a1344ae42eeb575f47cb4a0ccdd70483cdf046339","last_reissued_at":"2026-07-05T00:05:37.477600Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:05:37.477600Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1909.08763","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:05:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QbD0OfAM3T8ell8wAO4uDjA7nVoFTHmrQUwBGeGMzMsFAQ0gDeOZhD7ixd0R6c31h+nNih1IOorWY9bHXlpnAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T22:36:58.260603Z"},"content_sha256":"adceb8d638bce6a980f805cc6db158c3c3d0d4a5c5152e049d01915433b0c5ae","schema_version":"1.0","event_id":"sha256:adceb8d638bce6a980f805cc6db158c3c3d0d4a5c5152e049d01915433b0c5ae"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:MHWH4XGN4QNUSMI7XUVBGRFOIL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Bayesian Analysis of Multidimensional Functional Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Damla Senturk, Donatello Telesca, John Shamshoian, Shafali Jeste","submitted_at":"2019-09-19T01:23:03Z","abstract_excerpt":"Multi-dimensional functional data arises in numerous modern scientific experimental and observational studies. In this paper we focus on longitudinal functional data, a structured form of multidimensional functional data. Operating within a longitudinal functional framework we aim to capture low dimensional interpretable features. We propose a computationally efficient nonparametric Bayesian method to simultaneously smooth observed data, estimate conditional functional means and functional covariance surfaces. Statistical inference is based on Monte Carlo samples from the posterior measure thr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.08763","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/1909.08763/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:05:37Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"InZn1BesG47ov/uIkpaSCoAQpRT/1Y6SgQ0FZKhkRy8Lcxv/4uukUdDWkGBycXJY8ulwAxVv7rRkVcTlMqYSDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-31T22:36:58.260887Z"},"content_sha256":"13bdf23d53eabf8cb6183bb0fba5734fa5ab9a21e583f578c08e8ed281e6bdbb","schema_version":"1.0","event_id":"sha256:13bdf23d53eabf8cb6183bb0fba5734fa5ab9a21e583f578c08e8ed281e6bdbb"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/MHWH4XGN4QNUSMI7XUVBGRFOIL/bundle.json","state_url":"https://pith.science/pith/MHWH4XGN4QNUSMI7XUVBGRFOIL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/MHWH4XGN4QNUSMI7XUVBGRFOIL/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":"2026-07-31T22:36:58Z","links":{"resolver":"https://pith.science/pith/MHWH4XGN4QNUSMI7XUVBGRFOIL","bundle":"https://pith.science/pith/MHWH4XGN4QNUSMI7XUVBGRFOIL/bundle.json","state":"https://pith.science/pith/MHWH4XGN4QNUSMI7XUVBGRFOIL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/MHWH4XGN4QNUSMI7XUVBGRFOIL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:MHWH4XGN4QNUSMI7XUVBGRFOIL","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":"aaf65146a0c7071ce3e29d1e07b42582a7a17a7b32a497e85505f9a393c8503f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-09-19T01:23:03Z","title_canon_sha256":"26ccbe17ffad8c1ec8dcd611973b045e9681a491c0baba77e72863736a58a08f"},"schema_version":"1.0","source":{"id":"1909.08763","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1909.08763","created_at":"2026-07-05T00:05:37Z"},{"alias_kind":"arxiv_version","alias_value":"1909.08763v1","created_at":"2026-07-05T00:05:37Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1909.08763","created_at":"2026-07-05T00:05:37Z"},{"alias_kind":"pith_short_12","alias_value":"MHWH4XGN4QNU","created_at":"2026-07-05T00:05:37Z"},{"alias_kind":"pith_short_16","alias_value":"MHWH4XGN4QNUSMI7","created_at":"2026-07-05T00:05:37Z"},{"alias_kind":"pith_short_8","alias_value":"MHWH4XGN","created_at":"2026-07-05T00:05:37Z"}],"graph_snapshots":[{"event_id":"sha256:13bdf23d53eabf8cb6183bb0fba5734fa5ab9a21e583f578c08e8ed281e6bdbb","target":"graph","created_at":"2026-07-05T00:05:37Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1909.08763/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Multi-dimensional functional data arises in numerous modern scientific experimental and observational studies. In this paper we focus on longitudinal functional data, a structured form of multidimensional functional data. Operating within a longitudinal functional framework we aim to capture low dimensional interpretable features. We propose a computationally efficient nonparametric Bayesian method to simultaneously smooth observed data, estimate conditional functional means and functional covariance surfaces. Statistical inference is based on Monte Carlo samples from the posterior measure thr","authors_text":"Damla Senturk, Donatello Telesca, John Shamshoian, Shafali Jeste","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-09-19T01:23:03Z","title":"Bayesian Analysis of Multidimensional Functional Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1909.08763","kind":"arxiv","version":1},"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:adceb8d638bce6a980f805cc6db158c3c3d0d4a5c5152e049d01915433b0c5ae","target":"record","created_at":"2026-07-05T00:05:37Z","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":"aaf65146a0c7071ce3e29d1e07b42582a7a17a7b32a497e85505f9a393c8503f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2019-09-19T01:23:03Z","title_canon_sha256":"26ccbe17ffad8c1ec8dcd611973b045e9681a491c0baba77e72863736a58a08f"},"schema_version":"1.0","source":{"id":"1909.08763","kind":"arxiv","version":1}},"canonical_sha256":"61ec7e5ccde41b49311fbd2a1344ae42eeb575f47cb4a0ccdd70483cdf046339","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"61ec7e5ccde41b49311fbd2a1344ae42eeb575f47cb4a0ccdd70483cdf046339","first_computed_at":"2026-07-05T00:05:37.477600Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:05:37.477600Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"1nN6X9yjJ5g37RmmUIgRoD8FobomqXMLn/PT3rPp3l91NPmmv5UDxG8rx1soHAupBGI2pG06ZajOEN0CuKU4Dw==","signature_status":"signed_v1","signed_at":"2026-07-05T00:05:37.478088Z","signed_message":"canonical_sha256_bytes"},"source_id":"1909.08763","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:adceb8d638bce6a980f805cc6db158c3c3d0d4a5c5152e049d01915433b0c5ae","sha256:13bdf23d53eabf8cb6183bb0fba5734fa5ab9a21e583f578c08e8ed281e6bdbb"],"state_sha256":"07165f9d4f633cd711a19e0eae93f6c25ec868512a421b378be76c080471254a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"lu0jZmf5yfa0M2zpPWYhTSpX0uzr9O8xBRJ6i02FgfQWPjFA6ugugytLqosizdoVsf0+VR03aX/mDeFtVoc3BA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-31T22:36:58.263566Z","bundle_sha256":"0049b6bf5a23a59db44fd83452069759691958b846948ed446a877dbb22fadb2"}}