{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:SQEA4FQ3D3H3ORFVTTJOZXVKXU","short_pith_number":"pith:SQEA4FQ3","canonical_record":{"source":{"id":"1902.04304","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-02-12T09:52:48Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"77efa7a838a4cce9c107d7dc6d5ef5db94025079b7a5c51fbac1f222d772f29c","abstract_canon_sha256":"d3c843764564174bcea973deb19c1b6755012d5a9e157ea7e35e61df3bc5193c"},"schema_version":"1.0"},"canonical_sha256":"94080e161b1ecfb744b59cd2ecdeaabd21b4471ac7dc9d9d15e27da77cb136f2","source":{"kind":"arxiv","id":"1902.04304","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.04304","created_at":"2026-05-17T23:54:12Z"},{"alias_kind":"arxiv_version","alias_value":"1902.04304v1","created_at":"2026-05-17T23:54:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.04304","created_at":"2026-05-17T23:54:12Z"},{"alias_kind":"pith_short_12","alias_value":"SQEA4FQ3D3H3","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"SQEA4FQ3D3H3ORFV","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"SQEA4FQ3","created_at":"2026-05-18T12:33:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:SQEA4FQ3D3H3ORFVTTJOZXVKXU","target":"record","payload":{"canonical_record":{"source":{"id":"1902.04304","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-02-12T09:52:48Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"77efa7a838a4cce9c107d7dc6d5ef5db94025079b7a5c51fbac1f222d772f29c","abstract_canon_sha256":"d3c843764564174bcea973deb19c1b6755012d5a9e157ea7e35e61df3bc5193c"},"schema_version":"1.0"},"canonical_sha256":"94080e161b1ecfb744b59cd2ecdeaabd21b4471ac7dc9d9d15e27da77cb136f2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:54:12.622780Z","signature_b64":"2W3kw0QSFeiKaLbfgk9vsCKI2ApHDmd1Zyfasyu6vdxKdSViPmEIzcPC/C8mvm6t+F5lQPGRYzsi4grOaqUeAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"94080e161b1ecfb744b59cd2ecdeaabd21b4471ac7dc9d9d15e27da77cb136f2","last_reissued_at":"2026-05-17T23:54:12.622165Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:54:12.622165Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1902.04304","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":"2026-05-17T23:54:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"1vQ7qIlO6/yL2Hf9zugvitAC1Xh6U3MDS7+es2Y8IgRp7JiJmEbM4LKIGUCTiWA+JbUL4vimGc4mQkjoP7+eDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T11:58:34.615802Z"},"content_sha256":"0199eb5767253656099c69d9d183e5622a330c24582770d3e96bd614055ab383","schema_version":"1.0","event_id":"sha256:0199eb5767253656099c69d9d183e5622a330c24582770d3e96bd614055ab383"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:SQEA4FQ3D3H3ORFVTTJOZXVKXU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Statistical inference with F-statistics when fitting simple models to high-dimensional data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Hannes Leeb, Lukas Steinberger","submitted_at":"2019-02-12T09:52:48Z","abstract_excerpt":"We study linear subset regression in the context of the high-dimensional overall model $y = \\vartheta+\\theta' z + \\epsilon$ with univariate response $y$ and a $d$-vector of random regressors $z$, independent of $\\epsilon$. Here, \"high-dimensional\" means that the number $d$ of available explanatory variables is much larger than the number $n$ of observations. We consider simple linear sub-models where $y$ is regressed on a set of $p$ regressors given by $x = M'z$, for some $d \\times p$ matrix $M$ of full rank $p < n$. The corresponding simple model, i.e., $y=\\alpha+\\beta' x + e$, can be justifi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.04304","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":"2026-05-17T23:54:12Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dg/9omw/0J7RVvJGIhMqWGJhe6vSvUD0pJvSYZt9nJKSNrYmBHN0KjfqvCnK3nkmQDrGNwHba+j+UOPM30VICA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T11:58:34.616383Z"},"content_sha256":"e79118c54944a4a26b09fcab066afdc9a9111414f20a743ba4c0b93e98751fc5","schema_version":"1.0","event_id":"sha256:e79118c54944a4a26b09fcab066afdc9a9111414f20a743ba4c0b93e98751fc5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SQEA4FQ3D3H3ORFVTTJOZXVKXU/bundle.json","state_url":"https://pith.science/pith/SQEA4FQ3D3H3ORFVTTJOZXVKXU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SQEA4FQ3D3H3ORFVTTJOZXVKXU/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":"2026-05-26T11:58:34Z","links":{"resolver":"https://pith.science/pith/SQEA4FQ3D3H3ORFVTTJOZXVKXU","bundle":"https://pith.science/pith/SQEA4FQ3D3H3ORFVTTJOZXVKXU/bundle.json","state":"https://pith.science/pith/SQEA4FQ3D3H3ORFVTTJOZXVKXU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SQEA4FQ3D3H3ORFVTTJOZXVKXU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:SQEA4FQ3D3H3ORFVTTJOZXVKXU","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":"d3c843764564174bcea973deb19c1b6755012d5a9e157ea7e35e61df3bc5193c","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-02-12T09:52:48Z","title_canon_sha256":"77efa7a838a4cce9c107d7dc6d5ef5db94025079b7a5c51fbac1f222d772f29c"},"schema_version":"1.0","source":{"id":"1902.04304","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1902.04304","created_at":"2026-05-17T23:54:12Z"},{"alias_kind":"arxiv_version","alias_value":"1902.04304v1","created_at":"2026-05-17T23:54:12Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1902.04304","created_at":"2026-05-17T23:54:12Z"},{"alias_kind":"pith_short_12","alias_value":"SQEA4FQ3D3H3","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"SQEA4FQ3D3H3ORFV","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"SQEA4FQ3","created_at":"2026-05-18T12:33:27Z"}],"graph_snapshots":[{"event_id":"sha256:e79118c54944a4a26b09fcab066afdc9a9111414f20a743ba4c0b93e98751fc5","target":"graph","created_at":"2026-05-17T23:54:12Z","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":"We study linear subset regression in the context of the high-dimensional overall model $y = \\vartheta+\\theta' z + \\epsilon$ with univariate response $y$ and a $d$-vector of random regressors $z$, independent of $\\epsilon$. Here, \"high-dimensional\" means that the number $d$ of available explanatory variables is much larger than the number $n$ of observations. We consider simple linear sub-models where $y$ is regressed on a set of $p$ regressors given by $x = M'z$, for some $d \\times p$ matrix $M$ of full rank $p < n$. The corresponding simple model, i.e., $y=\\alpha+\\beta' x + e$, can be justifi","authors_text":"Hannes Leeb, Lukas Steinberger","cross_cats":["stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-02-12T09:52:48Z","title":"Statistical inference with F-statistics when fitting simple models to high-dimensional data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1902.04304","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":"sha256:0199eb5767253656099c69d9d183e5622a330c24582770d3e96bd614055ab383","target":"record","created_at":"2026-05-17T23:54:12Z","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":"d3c843764564174bcea973deb19c1b6755012d5a9e157ea7e35e61df3bc5193c","cross_cats_sorted":["stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-02-12T09:52:48Z","title_canon_sha256":"77efa7a838a4cce9c107d7dc6d5ef5db94025079b7a5c51fbac1f222d772f29c"},"schema_version":"1.0","source":{"id":"1902.04304","kind":"arxiv","version":1}},"canonical_sha256":"94080e161b1ecfb744b59cd2ecdeaabd21b4471ac7dc9d9d15e27da77cb136f2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"94080e161b1ecfb744b59cd2ecdeaabd21b4471ac7dc9d9d15e27da77cb136f2","first_computed_at":"2026-05-17T23:54:12.622165Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:54:12.622165Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"2W3kw0QSFeiKaLbfgk9vsCKI2ApHDmd1Zyfasyu6vdxKdSViPmEIzcPC/C8mvm6t+F5lQPGRYzsi4grOaqUeAg==","signature_status":"signed_v1","signed_at":"2026-05-17T23:54:12.622780Z","signed_message":"canonical_sha256_bytes"},"source_id":"1902.04304","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0199eb5767253656099c69d9d183e5622a330c24582770d3e96bd614055ab383","sha256:e79118c54944a4a26b09fcab066afdc9a9111414f20a743ba4c0b93e98751fc5"],"state_sha256":"cc28fd4feff8f8f86171a09b79ed1b163d16b38c46c7dcf3a63ea548ae13a502"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"w2o0finYKVvYZdYPPi5YHFvIbCXXESVugDfNO+SQS14W+NSBk8riamTXpaGzqK2X4nhNSRJja/swRLeeVxCCBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T11:58:34.619499Z","bundle_sha256":"7bc9eb5c4500fd5b1122262011b7757c013b79786629e190a08dfe96f5ddacf2"}}