{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:F3H4Z52IPRIMEC463SUMUXHG2E","short_pith_number":"pith:F3H4Z52I","canonical_record":{"source":{"id":"1302.6452","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-02-26T15:16:32Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7720c52d321e74286867b45d89a0c36f2cc270cad2bbb7e80faaa81ae48680ba","abstract_canon_sha256":"ae3079bfe73e1b1678f849b9b0139a1e9d90638a7ee674479d480761dbc992cc"},"schema_version":"1.0"},"canonical_sha256":"2ecfccf7487c50c20b9edca8ca5ce6d11b20d5a6cddb7f73a2b6da2629346ce9","source":{"kind":"arxiv","id":"1302.6452","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1302.6452","created_at":"2026-05-18T03:32:29Z"},{"alias_kind":"arxiv_version","alias_value":"1302.6452v1","created_at":"2026-05-18T03:32:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1302.6452","created_at":"2026-05-18T03:32:29Z"},{"alias_kind":"pith_short_12","alias_value":"F3H4Z52IPRIM","created_at":"2026-05-18T12:27:43Z"},{"alias_kind":"pith_short_16","alias_value":"F3H4Z52IPRIMEC46","created_at":"2026-05-18T12:27:43Z"},{"alias_kind":"pith_short_8","alias_value":"F3H4Z52I","created_at":"2026-05-18T12:27:43Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:F3H4Z52IPRIMEC463SUMUXHG2E","target":"record","payload":{"canonical_record":{"source":{"id":"1302.6452","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-02-26T15:16:32Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7720c52d321e74286867b45d89a0c36f2cc270cad2bbb7e80faaa81ae48680ba","abstract_canon_sha256":"ae3079bfe73e1b1678f849b9b0139a1e9d90638a7ee674479d480761dbc992cc"},"schema_version":"1.0"},"canonical_sha256":"2ecfccf7487c50c20b9edca8ca5ce6d11b20d5a6cddb7f73a2b6da2629346ce9","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:32:29.180698Z","signature_b64":"3X42nCKVy8WJQiKSKYOZGAFJQGZIy1AiQBXXx6LV1ZK9zYIIUbWTNPeWF1OiSIOFEUo3CHKGvJf3Igj60MdVBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2ecfccf7487c50c20b9edca8ca5ce6d11b20d5a6cddb7f73a2b6da2629346ce9","last_reissued_at":"2026-05-18T03:32:29.180003Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:32:29.180003Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1302.6452","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":"2026-05-18T03:32:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cSu3uIe92RS/FNWIMsx/tiUGb/kpaRrPDDjaIJGtQDG5/XOB8xfUGom6b9ghm7iVX/vkfpn7rRxB0ryXweBZAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T10:34:05.727359Z"},"content_sha256":"ba6f347be06b0f8d5a540634bbdaad800256839bc30a01529eadb9bbd8bd46e7","schema_version":"1.0","event_id":"sha256:ba6f347be06b0f8d5a540634bbdaad800256839bc30a01529eadb9bbd8bd46e7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:F3H4Z52IPRIMEC463SUMUXHG2E","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Conformal Prediction Approach to Explore Functional Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Alessandro Rinaldo, Jing Lei, Larry Wasserman","submitted_at":"2013-02-26T15:16:32Z","abstract_excerpt":"This paper applies conformal prediction techniques to compute simultaneous prediction bands and clustering trees for functional data. These tools can be used to detect outliers and clusters. Both our prediction bands and clustering trees provide prediction sets for the underlying stochastic process with a guaranteed finite sample behavior, under no distributional assumptions. The prediction sets are also informative in that they correspond to the high density region of the underlying process. While ordinary conformal prediction has high computational cost for functional data, we use the induct"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1302.6452","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":""},"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":"2026-05-18T03:32:29Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"E+3aesbpwEXOUDaRySAaozBs0dLbeam8UL35E8TDPVtLzgMhtiNQ8uRPaAj1VH3/GRnAZAvrTpS27J3CwZ18AQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T10:34:05.727710Z"},"content_sha256":"ea1b0f8c368365614d91fa63e08679542ffbc318e2fa4d1fffb1f49445266e31","schema_version":"1.0","event_id":"sha256:ea1b0f8c368365614d91fa63e08679542ffbc318e2fa4d1fffb1f49445266e31"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F3H4Z52IPRIMEC463SUMUXHG2E/bundle.json","state_url":"https://pith.science/pith/F3H4Z52IPRIMEC463SUMUXHG2E/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F3H4Z52IPRIMEC463SUMUXHG2E/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":"2026-05-20T10:34:05Z","links":{"resolver":"https://pith.science/pith/F3H4Z52IPRIMEC463SUMUXHG2E","bundle":"https://pith.science/pith/F3H4Z52IPRIMEC463SUMUXHG2E/bundle.json","state":"https://pith.science/pith/F3H4Z52IPRIMEC463SUMUXHG2E/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F3H4Z52IPRIMEC463SUMUXHG2E/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:F3H4Z52IPRIMEC463SUMUXHG2E","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":"ae3079bfe73e1b1678f849b9b0139a1e9d90638a7ee674479d480761dbc992cc","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-02-26T15:16:32Z","title_canon_sha256":"7720c52d321e74286867b45d89a0c36f2cc270cad2bbb7e80faaa81ae48680ba"},"schema_version":"1.0","source":{"id":"1302.6452","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1302.6452","created_at":"2026-05-18T03:32:29Z"},{"alias_kind":"arxiv_version","alias_value":"1302.6452v1","created_at":"2026-05-18T03:32:29Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1302.6452","created_at":"2026-05-18T03:32:29Z"},{"alias_kind":"pith_short_12","alias_value":"F3H4Z52IPRIM","created_at":"2026-05-18T12:27:43Z"},{"alias_kind":"pith_short_16","alias_value":"F3H4Z52IPRIMEC46","created_at":"2026-05-18T12:27:43Z"},{"alias_kind":"pith_short_8","alias_value":"F3H4Z52I","created_at":"2026-05-18T12:27:43Z"}],"graph_snapshots":[{"event_id":"sha256:ea1b0f8c368365614d91fa63e08679542ffbc318e2fa4d1fffb1f49445266e31","target":"graph","created_at":"2026-05-18T03:32:29Z","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":"This paper applies conformal prediction techniques to compute simultaneous prediction bands and clustering trees for functional data. These tools can be used to detect outliers and clusters. Both our prediction bands and clustering trees provide prediction sets for the underlying stochastic process with a guaranteed finite sample behavior, under no distributional assumptions. The prediction sets are also informative in that they correspond to the high density region of the underlying process. While ordinary conformal prediction has high computational cost for functional data, we use the induct","authors_text":"Alessandro Rinaldo, Jing Lei, Larry Wasserman","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-02-26T15:16:32Z","title":"A Conformal Prediction Approach to Explore Functional Data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1302.6452","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:ba6f347be06b0f8d5a540634bbdaad800256839bc30a01529eadb9bbd8bd46e7","target":"record","created_at":"2026-05-18T03:32:29Z","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":"ae3079bfe73e1b1678f849b9b0139a1e9d90638a7ee674479d480761dbc992cc","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2013-02-26T15:16:32Z","title_canon_sha256":"7720c52d321e74286867b45d89a0c36f2cc270cad2bbb7e80faaa81ae48680ba"},"schema_version":"1.0","source":{"id":"1302.6452","kind":"arxiv","version":1}},"canonical_sha256":"2ecfccf7487c50c20b9edca8ca5ce6d11b20d5a6cddb7f73a2b6da2629346ce9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2ecfccf7487c50c20b9edca8ca5ce6d11b20d5a6cddb7f73a2b6da2629346ce9","first_computed_at":"2026-05-18T03:32:29.180003Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:32:29.180003Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"3X42nCKVy8WJQiKSKYOZGAFJQGZIy1AiQBXXx6LV1ZK9zYIIUbWTNPeWF1OiSIOFEUo3CHKGvJf3Igj60MdVBw==","signature_status":"signed_v1","signed_at":"2026-05-18T03:32:29.180698Z","signed_message":"canonical_sha256_bytes"},"source_id":"1302.6452","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:ba6f347be06b0f8d5a540634bbdaad800256839bc30a01529eadb9bbd8bd46e7","sha256:ea1b0f8c368365614d91fa63e08679542ffbc318e2fa4d1fffb1f49445266e31"],"state_sha256":"bef3aaa5e5109a864b43556c8ddbe003cd28588faa9e4e204bc483d63a48f1b2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"FH//CK5bGaZkAHusordOI5FF+sHDkIPfDqFQSH911au0UFh1lX9e0Sqy8JneR/N2wcxTMvrkz9nmjSr1BbwzAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-20T10:34:05.729688Z","bundle_sha256":"7f01ffeb64583399cbf03fb25058f8304f9faf770c117b52beac1c5c122b9c4c"}}