{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:UZBH5JPFZJYZNYJY5ZNWWS6TZ4","short_pith_number":"pith:UZBH5JPF","canonical_record":{"source":{"id":"1603.05758","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-03-18T03:37:57Z","cross_cats_sorted":[],"title_canon_sha256":"db441500a722ba11bc81a9eea28ea76efb626b5eaea14ad7874a0d02e037f3c4","abstract_canon_sha256":"27cc51218ca71426d57252d1d2aa0a47813743f4b443b8109fe255fd427afe2f"},"schema_version":"1.0"},"canonical_sha256":"a6427ea5e5ca7196e138ee5b6b4bd3cf002c7c81d8c82a6cfb8fb044f7ca94bb","source":{"kind":"arxiv","id":"1603.05758","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1603.05758","created_at":"2026-05-18T00:46:55Z"},{"alias_kind":"arxiv_version","alias_value":"1603.05758v2","created_at":"2026-05-18T00:46:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.05758","created_at":"2026-05-18T00:46:55Z"},{"alias_kind":"pith_short_12","alias_value":"UZBH5JPFZJYZ","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_16","alias_value":"UZBH5JPFZJYZNYJY","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_8","alias_value":"UZBH5JPF","created_at":"2026-05-18T12:30:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:UZBH5JPFZJYZNYJY5ZNWWS6TZ4","target":"record","payload":{"canonical_record":{"source":{"id":"1603.05758","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-03-18T03:37:57Z","cross_cats_sorted":[],"title_canon_sha256":"db441500a722ba11bc81a9eea28ea76efb626b5eaea14ad7874a0d02e037f3c4","abstract_canon_sha256":"27cc51218ca71426d57252d1d2aa0a47813743f4b443b8109fe255fd427afe2f"},"schema_version":"1.0"},"canonical_sha256":"a6427ea5e5ca7196e138ee5b6b4bd3cf002c7c81d8c82a6cfb8fb044f7ca94bb","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:46:55.785014Z","signature_b64":"9zkympbJM4XtJnQnZPAYjMqgom1dYLV6a1kMaIbPM1RGNF7PnciBVy9LlXC50UlTQybB2W0HH8swSSfZt7F7DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a6427ea5e5ca7196e138ee5b6b4bd3cf002c7c81d8c82a6cfb8fb044f7ca94bb","last_reissued_at":"2026-05-18T00:46:55.784592Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:46:55.784592Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1603.05758","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:46:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZVK/OFG+KCAl4BxFstk407MHFmTom8Ol4HoTXQoM67PL/OPMqwYUzbHBT1/fu7yUygzsAn9/h159HsZmBzrkAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T02:41:32.239972Z"},"content_sha256":"17884f73f6c733feb8322d9262a96829d3e0634eb736c994c93f21e72ded833d","schema_version":"1.0","event_id":"sha256:17884f73f6c733feb8322d9262a96829d3e0634eb736c994c93f21e72ded833d"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:UZBH5JPFZJYZNYJY5ZNWWS6TZ4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fast Covariance Estimation for Sparse Functional Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Cai Li, Ciprian M. Crainiceanu, Luo Xiao, William Checkley","submitted_at":"2016-03-18T03:37:57Z","abstract_excerpt":"Smoothing of noisy sample covariances is an important component in functional data analysis. We propose a novel covariance smoothing method based on penalized splines and associated software. The proposed method is a bivariate spline smoother that is designed for covariance smoothing and can be used for sparse functional or longitudinal data. We propose a fast algorithm for covariance smoothing using leave-one-subject-out cross validation. Our simulations show that the proposed method compares favorably against several commonly used methods. The method is applied to a study of child growth led"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.05758","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":""},"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":"2026-05-18T00:46:55Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JFTLpJacdP7ETgyJMZguArc6s2vtKtyNVSOvk1pM7AzkGYDspBEAWuI/ra/QVl3li0A7HWlT5TAB0CZXVPoUBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-03T02:41:32.240331Z"},"content_sha256":"aefb136ea61fd59b2d6b781124358bfa757b11cba7b223283585bf3ed8b5ee3b","schema_version":"1.0","event_id":"sha256:aefb136ea61fd59b2d6b781124358bfa757b11cba7b223283585bf3ed8b5ee3b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UZBH5JPFZJYZNYJY5ZNWWS6TZ4/bundle.json","state_url":"https://pith.science/pith/UZBH5JPFZJYZNYJY5ZNWWS6TZ4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UZBH5JPFZJYZNYJY5ZNWWS6TZ4/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":"2026-07-03T02:41:32Z","links":{"resolver":"https://pith.science/pith/UZBH5JPFZJYZNYJY5ZNWWS6TZ4","bundle":"https://pith.science/pith/UZBH5JPFZJYZNYJY5ZNWWS6TZ4/bundle.json","state":"https://pith.science/pith/UZBH5JPFZJYZNYJY5ZNWWS6TZ4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UZBH5JPFZJYZNYJY5ZNWWS6TZ4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:UZBH5JPFZJYZNYJY5ZNWWS6TZ4","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":"27cc51218ca71426d57252d1d2aa0a47813743f4b443b8109fe255fd427afe2f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-03-18T03:37:57Z","title_canon_sha256":"db441500a722ba11bc81a9eea28ea76efb626b5eaea14ad7874a0d02e037f3c4"},"schema_version":"1.0","source":{"id":"1603.05758","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1603.05758","created_at":"2026-05-18T00:46:55Z"},{"alias_kind":"arxiv_version","alias_value":"1603.05758v2","created_at":"2026-05-18T00:46:55Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1603.05758","created_at":"2026-05-18T00:46:55Z"},{"alias_kind":"pith_short_12","alias_value":"UZBH5JPFZJYZ","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_16","alias_value":"UZBH5JPFZJYZNYJY","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_8","alias_value":"UZBH5JPF","created_at":"2026-05-18T12:30:46Z"}],"graph_snapshots":[{"event_id":"sha256:aefb136ea61fd59b2d6b781124358bfa757b11cba7b223283585bf3ed8b5ee3b","target":"graph","created_at":"2026-05-18T00:46:55Z","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":"Smoothing of noisy sample covariances is an important component in functional data analysis. We propose a novel covariance smoothing method based on penalized splines and associated software. The proposed method is a bivariate spline smoother that is designed for covariance smoothing and can be used for sparse functional or longitudinal data. We propose a fast algorithm for covariance smoothing using leave-one-subject-out cross validation. Our simulations show that the proposed method compares favorably against several commonly used methods. The method is applied to a study of child growth led","authors_text":"Cai Li, Ciprian M. Crainiceanu, Luo Xiao, William Checkley","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-03-18T03:37:57Z","title":"Fast Covariance Estimation for Sparse Functional Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1603.05758","kind":"arxiv","version":2},"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":"sha256:17884f73f6c733feb8322d9262a96829d3e0634eb736c994c93f21e72ded833d","target":"record","created_at":"2026-05-18T00:46:55Z","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":"27cc51218ca71426d57252d1d2aa0a47813743f4b443b8109fe255fd427afe2f","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-03-18T03:37:57Z","title_canon_sha256":"db441500a722ba11bc81a9eea28ea76efb626b5eaea14ad7874a0d02e037f3c4"},"schema_version":"1.0","source":{"id":"1603.05758","kind":"arxiv","version":2}},"canonical_sha256":"a6427ea5e5ca7196e138ee5b6b4bd3cf002c7c81d8c82a6cfb8fb044f7ca94bb","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a6427ea5e5ca7196e138ee5b6b4bd3cf002c7c81d8c82a6cfb8fb044f7ca94bb","first_computed_at":"2026-05-18T00:46:55.784592Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:46:55.784592Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"9zkympbJM4XtJnQnZPAYjMqgom1dYLV6a1kMaIbPM1RGNF7PnciBVy9LlXC50UlTQybB2W0HH8swSSfZt7F7DA==","signature_status":"signed_v1","signed_at":"2026-05-18T00:46:55.785014Z","signed_message":"canonical_sha256_bytes"},"source_id":"1603.05758","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:17884f73f6c733feb8322d9262a96829d3e0634eb736c994c93f21e72ded833d","sha256:aefb136ea61fd59b2d6b781124358bfa757b11cba7b223283585bf3ed8b5ee3b"],"state_sha256":"60eabecdb336356445fcfec390f303d214dd1e1a0e0bda4cace3eed7adbd407b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hJc/5SFcLvpni0w+HbedTc/JEmAXj6Uwm7QVs7Rv7csEod7k9lxdLLYAi4hylPTD08EhHq6MP4PAeGoFwi8gDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-03T02:41:32.242473Z","bundle_sha256":"1c7daeebd411c021c0b7fe929898aedc23a402abd84500ce5c115d417dd995bc"}}