{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2013:BT4H4GKNKYE2XBTKRCS7552QNB","short_pith_number":"pith:BT4H4GKN","canonical_record":{"source":{"id":"1306.3171","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2013-06-13T17:19:39Z","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"title_canon_sha256":"2240ac5927d323af152ad390d409fbf42beed8e1a81c6cae3246a4378aafcf46","abstract_canon_sha256":"7d7752daf6edb5418dd0fd71f1ad9aff4d793dee75389dafc900e0d9b61e587d"},"schema_version":"1.0"},"canonical_sha256":"0cf87e194d5609ab866a88a5fef75068789505c4f6aebd9232f470ce1eea7ea7","source":{"kind":"arxiv","id":"1306.3171","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1306.3171","created_at":"2026-05-18T02:55:04Z"},{"alias_kind":"arxiv_version","alias_value":"1306.3171v2","created_at":"2026-05-18T02:55:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1306.3171","created_at":"2026-05-18T02:55:04Z"},{"alias_kind":"pith_short_12","alias_value":"BT4H4GKNKYE2","created_at":"2026-05-18T12:27:40Z"},{"alias_kind":"pith_short_16","alias_value":"BT4H4GKNKYE2XBTK","created_at":"2026-05-18T12:27:40Z"},{"alias_kind":"pith_short_8","alias_value":"BT4H4GKN","created_at":"2026-05-18T12:27:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2013:BT4H4GKNKYE2XBTKRCS7552QNB","target":"record","payload":{"canonical_record":{"source":{"id":"1306.3171","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2013-06-13T17:19:39Z","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"title_canon_sha256":"2240ac5927d323af152ad390d409fbf42beed8e1a81c6cae3246a4378aafcf46","abstract_canon_sha256":"7d7752daf6edb5418dd0fd71f1ad9aff4d793dee75389dafc900e0d9b61e587d"},"schema_version":"1.0"},"canonical_sha256":"0cf87e194d5609ab866a88a5fef75068789505c4f6aebd9232f470ce1eea7ea7","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T02:55:04.179009Z","signature_b64":"0ZIEeMCXjiH7Xw274IHaz/w7C4NKIWG275WUUBWly4kziXEUSzAOmQO7zIP/43cN+uKAxnwZh/2rbJeSnk/ABg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"0cf87e194d5609ab866a88a5fef75068789505c4f6aebd9232f470ce1eea7ea7","last_reissued_at":"2026-05-18T02:55:04.178352Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T02:55:04.178352Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1306.3171","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":"2026-05-18T02:55:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cZ8WRi3RP+/4i0YGQ9mQ81Hr+GzvPMJuJHzbOerDi0WwuILSnJyMUg1KNJXujyecq1nWSoRJfvrVqXwU8ombAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T10:10:30.053196Z"},"content_sha256":"00f23110c7eacab543f8714aefd160312e35cf50932c450fd6b8da1860cebb66","schema_version":"1.0","event_id":"sha256:00f23110c7eacab543f8714aefd160312e35cf50932c450fd6b8da1860cebb66"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2013:BT4H4GKNKYE2XBTKRCS7552QNB","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Confidence Intervals and Hypothesis Testing for High-Dimensional Regression","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.IT","cs.LG","math.IT"],"primary_cat":"stat.ME","authors_text":"Adel Javanmard, Andrea Montanari","submitted_at":"2013-06-13T17:19:39Z","abstract_excerpt":"Fitting high-dimensional statistical models often requires the use of non-linear parameter estimation procedures. As a consequence, it is generally impossible to obtain an exact characterization of the probability distribution of the parameter estimates. This in turn implies that it is extremely challenging to quantify the \\emph{uncertainty} associated with a certain parameter estimate. Concretely, no commonly accepted procedure exists for computing classical measures of uncertainty and statistical significance as confidence intervals or $p$-values for these models.\n  We consider here high-dim"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1306.3171","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":"2026-05-18T02:55:04Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fBlo7DS9fQNwMdvpknFTSP11BZoiIAQDIVAIrB1GuiFn7w+io2ugd7dAL97lVA9vwWxPksXHVz6COyi7sM15Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-26T10:10:30.053915Z"},"content_sha256":"2c87bbe2b83c44dea7007c8c5d6f31d1f7c5a17f254e77845ea3e41b1f901484","schema_version":"1.0","event_id":"sha256:2c87bbe2b83c44dea7007c8c5d6f31d1f7c5a17f254e77845ea3e41b1f901484"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/BT4H4GKNKYE2XBTKRCS7552QNB/bundle.json","state_url":"https://pith.science/pith/BT4H4GKNKYE2XBTKRCS7552QNB/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/BT4H4GKNKYE2XBTKRCS7552QNB/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":"2026-05-26T10:10:30Z","links":{"resolver":"https://pith.science/pith/BT4H4GKNKYE2XBTKRCS7552QNB","bundle":"https://pith.science/pith/BT4H4GKNKYE2XBTKRCS7552QNB/bundle.json","state":"https://pith.science/pith/BT4H4GKNKYE2XBTKRCS7552QNB/state.json","well_known_bundle":"https://pith.science/.well-known/pith/BT4H4GKNKYE2XBTKRCS7552QNB/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2013:BT4H4GKNKYE2XBTKRCS7552QNB","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":"7d7752daf6edb5418dd0fd71f1ad9aff4d793dee75389dafc900e0d9b61e587d","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2013-06-13T17:19:39Z","title_canon_sha256":"2240ac5927d323af152ad390d409fbf42beed8e1a81c6cae3246a4378aafcf46"},"schema_version":"1.0","source":{"id":"1306.3171","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1306.3171","created_at":"2026-05-18T02:55:04Z"},{"alias_kind":"arxiv_version","alias_value":"1306.3171v2","created_at":"2026-05-18T02:55:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1306.3171","created_at":"2026-05-18T02:55:04Z"},{"alias_kind":"pith_short_12","alias_value":"BT4H4GKNKYE2","created_at":"2026-05-18T12:27:40Z"},{"alias_kind":"pith_short_16","alias_value":"BT4H4GKNKYE2XBTK","created_at":"2026-05-18T12:27:40Z"},{"alias_kind":"pith_short_8","alias_value":"BT4H4GKN","created_at":"2026-05-18T12:27:40Z"}],"graph_snapshots":[{"event_id":"sha256:2c87bbe2b83c44dea7007c8c5d6f31d1f7c5a17f254e77845ea3e41b1f901484","target":"graph","created_at":"2026-05-18T02:55:04Z","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":"Fitting high-dimensional statistical models often requires the use of non-linear parameter estimation procedures. As a consequence, it is generally impossible to obtain an exact characterization of the probability distribution of the parameter estimates. This in turn implies that it is extremely challenging to quantify the \\emph{uncertainty} associated with a certain parameter estimate. Concretely, no commonly accepted procedure exists for computing classical measures of uncertainty and statistical significance as confidence intervals or $p$-values for these models.\n  We consider here high-dim","authors_text":"Adel Javanmard, Andrea Montanari","cross_cats":["cs.IT","cs.LG","math.IT"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2013-06-13T17:19:39Z","title":"Confidence Intervals and Hypothesis Testing for High-Dimensional Regression"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1306.3171","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":"sha256:00f23110c7eacab543f8714aefd160312e35cf50932c450fd6b8da1860cebb66","target":"record","created_at":"2026-05-18T02:55:04Z","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":"7d7752daf6edb5418dd0fd71f1ad9aff4d793dee75389dafc900e0d9b61e587d","cross_cats_sorted":["cs.IT","cs.LG","math.IT"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2013-06-13T17:19:39Z","title_canon_sha256":"2240ac5927d323af152ad390d409fbf42beed8e1a81c6cae3246a4378aafcf46"},"schema_version":"1.0","source":{"id":"1306.3171","kind":"arxiv","version":2}},"canonical_sha256":"0cf87e194d5609ab866a88a5fef75068789505c4f6aebd9232f470ce1eea7ea7","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"0cf87e194d5609ab866a88a5fef75068789505c4f6aebd9232f470ce1eea7ea7","first_computed_at":"2026-05-18T02:55:04.178352Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T02:55:04.178352Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"0ZIEeMCXjiH7Xw274IHaz/w7C4NKIWG275WUUBWly4kziXEUSzAOmQO7zIP/43cN+uKAxnwZh/2rbJeSnk/ABg==","signature_status":"signed_v1","signed_at":"2026-05-18T02:55:04.179009Z","signed_message":"canonical_sha256_bytes"},"source_id":"1306.3171","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:00f23110c7eacab543f8714aefd160312e35cf50932c450fd6b8da1860cebb66","sha256:2c87bbe2b83c44dea7007c8c5d6f31d1f7c5a17f254e77845ea3e41b1f901484"],"state_sha256":"3805b5d2b2ce08a0d0a481c1f3ebf8b3927109d31dd9a7865b59be0a3e07fb8e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YGEYA2bWZtDNnCtoKmiP7KG1o4ldCWKfhpO/rtrLgcHKWTp72+r9Z1R8AuGHRGVj8YPK7qjsr2Qj7qc0H/5+AQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-26T10:10:30.058159Z","bundle_sha256":"f1cac332cead0482214d2a78e71df5da4466ba0c36ac1698fecd16c69367d8fe"}}