{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:YITSXXZNHJCQV63GZYRS6BKJML","short_pith_number":"pith:YITSXXZN","canonical_record":{"source":{"id":"1302.5856","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2013-02-23T23:05:40Z","cross_cats_sorted":[],"title_canon_sha256":"fc8e313e14d9b034be6a096e03b9c4a94378a8688457b23f3027a7d9e1c1ab5c","abstract_canon_sha256":"2c5299a9bae335fd50f393c0529e5c0d452809404e9d4e729038baa00f90bef9"},"schema_version":"1.0"},"canonical_sha256":"c2272bdf2d3a450afb66ce232f054962d362f2beadaa7c631a72dc0a6af5f39f","source":{"kind":"arxiv","id":"1302.5856","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1302.5856","created_at":"2026-05-18T03:32:43Z"},{"alias_kind":"arxiv_version","alias_value":"1302.5856v1","created_at":"2026-05-18T03:32:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1302.5856","created_at":"2026-05-18T03:32:43Z"},{"alias_kind":"pith_short_12","alias_value":"YITSXXZNHJCQ","created_at":"2026-05-18T12:28:06Z"},{"alias_kind":"pith_short_16","alias_value":"YITSXXZNHJCQV63G","created_at":"2026-05-18T12:28:06Z"},{"alias_kind":"pith_short_8","alias_value":"YITSXXZN","created_at":"2026-05-18T12:28:06Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:YITSXXZNHJCQV63GZYRS6BKJML","target":"record","payload":{"canonical_record":{"source":{"id":"1302.5856","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2013-02-23T23:05:40Z","cross_cats_sorted":[],"title_canon_sha256":"fc8e313e14d9b034be6a096e03b9c4a94378a8688457b23f3027a7d9e1c1ab5c","abstract_canon_sha256":"2c5299a9bae335fd50f393c0529e5c0d452809404e9d4e729038baa00f90bef9"},"schema_version":"1.0"},"canonical_sha256":"c2272bdf2d3a450afb66ce232f054962d362f2beadaa7c631a72dc0a6af5f39f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:32:43.456810Z","signature_b64":"Gia/AtHNlDdVoaGUKc9X03XGpUrODGEwCB1zLzNlzG9hdlj7KTk8MLCVU5OU2kA8p1O08h9TUQQwcij56sMTDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c2272bdf2d3a450afb66ce232f054962d362f2beadaa7c631a72dc0a6af5f39f","last_reissued_at":"2026-05-18T03:32:43.455826Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:32:43.455826Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1302.5856","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":"2026-05-18T03:32:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"y6gOYoA2ARtoA1qI0RoxUhvlC070tDMUOMRKvM11d2OxDl3IDh1RIDzphBpyoQcgIj1lpr5wTR1FSkOwsTX8Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T13:53:03.977825Z"},"content_sha256":"175ba226f1f57cd95d5e45340d0fd486f3afa53bee820d16a59831e9168b65fe","schema_version":"1.0","event_id":"sha256:175ba226f1f57cd95d5e45340d0fd486f3afa53bee820d16a59831e9168b65fe"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:YITSXXZNHJCQV63GZYRS6BKJML","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A PRESS statistic for two-block partial least squares regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Brian McWilliams, Giovanni Montana","submitted_at":"2013-02-23T23:05:40Z","abstract_excerpt":"Predictive modelling of multivariate data where both the covariates and responses are high-dimensional is becoming an increasingly popular task in many data mining applications. Partial Least Squares (PLS) regression often turns out to be a useful model in these situations since it performs dimensionality reduction by assuming the existence of a small number of latent factors that may explain the linear dependence between input and output. In practice, the number of latent factors to be retained, which controls the complexity of the model and its predictive ability, has to be carefully selecte"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1302.5856","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":"2026-05-18T03:32:43Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+ubrMY0j0lNMTID2Jz3nzdY6tZZdXyHZRtGG7HubhC+5a3NNi0GbaVPpONheMtpqOAqQINJL327wJWUOdmQ5AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-30T13:53:03.978168Z"},"content_sha256":"39cc2ae2d2d426797b846fd6039f49eec4b71734a809af4a651d8326643047b0","schema_version":"1.0","event_id":"sha256:39cc2ae2d2d426797b846fd6039f49eec4b71734a809af4a651d8326643047b0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/YITSXXZNHJCQV63GZYRS6BKJML/bundle.json","state_url":"https://pith.science/pith/YITSXXZNHJCQV63GZYRS6BKJML/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/YITSXXZNHJCQV63GZYRS6BKJML/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":"2026-05-30T13:53:03Z","links":{"resolver":"https://pith.science/pith/YITSXXZNHJCQV63GZYRS6BKJML","bundle":"https://pith.science/pith/YITSXXZNHJCQV63GZYRS6BKJML/bundle.json","state":"https://pith.science/pith/YITSXXZNHJCQV63GZYRS6BKJML/state.json","well_known_bundle":"https://pith.science/.well-known/pith/YITSXXZNHJCQV63GZYRS6BKJML/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:YITSXXZNHJCQV63GZYRS6BKJML","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":"2c5299a9bae335fd50f393c0529e5c0d452809404e9d4e729038baa00f90bef9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2013-02-23T23:05:40Z","title_canon_sha256":"fc8e313e14d9b034be6a096e03b9c4a94378a8688457b23f3027a7d9e1c1ab5c"},"schema_version":"1.0","source":{"id":"1302.5856","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1302.5856","created_at":"2026-05-18T03:32:43Z"},{"alias_kind":"arxiv_version","alias_value":"1302.5856v1","created_at":"2026-05-18T03:32:43Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1302.5856","created_at":"2026-05-18T03:32:43Z"},{"alias_kind":"pith_short_12","alias_value":"YITSXXZNHJCQ","created_at":"2026-05-18T12:28:06Z"},{"alias_kind":"pith_short_16","alias_value":"YITSXXZNHJCQV63G","created_at":"2026-05-18T12:28:06Z"},{"alias_kind":"pith_short_8","alias_value":"YITSXXZN","created_at":"2026-05-18T12:28:06Z"}],"graph_snapshots":[{"event_id":"sha256:39cc2ae2d2d426797b846fd6039f49eec4b71734a809af4a651d8326643047b0","target":"graph","created_at":"2026-05-18T03:32:43Z","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":"Predictive modelling of multivariate data where both the covariates and responses are high-dimensional is becoming an increasingly popular task in many data mining applications. Partial Least Squares (PLS) regression often turns out to be a useful model in these situations since it performs dimensionality reduction by assuming the existence of a small number of latent factors that may explain the linear dependence between input and output. In practice, the number of latent factors to be retained, which controls the complexity of the model and its predictive ability, has to be carefully selecte","authors_text":"Brian McWilliams, Giovanni Montana","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2013-02-23T23:05:40Z","title":"A PRESS statistic for two-block partial least squares regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1302.5856","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":"sha256:175ba226f1f57cd95d5e45340d0fd486f3afa53bee820d16a59831e9168b65fe","target":"record","created_at":"2026-05-18T03:32:43Z","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":"2c5299a9bae335fd50f393c0529e5c0d452809404e9d4e729038baa00f90bef9","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2013-02-23T23:05:40Z","title_canon_sha256":"fc8e313e14d9b034be6a096e03b9c4a94378a8688457b23f3027a7d9e1c1ab5c"},"schema_version":"1.0","source":{"id":"1302.5856","kind":"arxiv","version":1}},"canonical_sha256":"c2272bdf2d3a450afb66ce232f054962d362f2beadaa7c631a72dc0a6af5f39f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c2272bdf2d3a450afb66ce232f054962d362f2beadaa7c631a72dc0a6af5f39f","first_computed_at":"2026-05-18T03:32:43.455826Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T03:32:43.455826Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Gia/AtHNlDdVoaGUKc9X03XGpUrODGEwCB1zLzNlzG9hdlj7KTk8MLCVU5OU2kA8p1O08h9TUQQwcij56sMTDA==","signature_status":"signed_v1","signed_at":"2026-05-18T03:32:43.456810Z","signed_message":"canonical_sha256_bytes"},"source_id":"1302.5856","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:175ba226f1f57cd95d5e45340d0fd486f3afa53bee820d16a59831e9168b65fe","sha256:39cc2ae2d2d426797b846fd6039f49eec4b71734a809af4a651d8326643047b0"],"state_sha256":"7426d2020049c9ca94ec2b185c5b49d73d6dfb6081618c7d91ddba8b7addc578"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nhML+fC+AKb6BcMwH+FOTicbrA4AqNeW9QK2O8BskDRwFxfD90K35j5mdbH1dfJgoT+4nTp00zMBqnftN2KqBw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-30T13:53:03.980075Z","bundle_sha256":"269342a49834dae112a491e2357b1bbc3fb7e73e1a30337f632422c09b0cb412"}}