{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2014:W6YQFAPBPYSPIC3XKVW5P4T45A","short_pith_number":"pith:W6YQFAPB","canonical_record":{"source":{"id":"1405.3319","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-05-13T22:31:09Z","cross_cats_sorted":["stat.ME","stat.ML"],"title_canon_sha256":"a78077fa97c0379901abbd3d52e7105b1db8656aec434a0294e144fc0dc915e2","abstract_canon_sha256":"6412a10975e516bee0889351a08c72c4218a7d8d108b4ef9aaf3b67a0239bdf5"},"schema_version":"1.0"},"canonical_sha256":"b7b10281e17e24f40b77556dd7f27ce812cc8b28b9de2c3c8e8cd170997f27da","source":{"kind":"arxiv","id":"1405.3319","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1405.3319","created_at":"2026-05-18T00:10:24Z"},{"alias_kind":"arxiv_version","alias_value":"1405.3319v4","created_at":"2026-05-18T00:10:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1405.3319","created_at":"2026-05-18T00:10:24Z"},{"alias_kind":"pith_short_12","alias_value":"W6YQFAPBPYSP","created_at":"2026-05-18T12:28:54Z"},{"alias_kind":"pith_short_16","alias_value":"W6YQFAPBPYSPIC3X","created_at":"2026-05-18T12:28:54Z"},{"alias_kind":"pith_short_8","alias_value":"W6YQFAPB","created_at":"2026-05-18T12:28:54Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2014:W6YQFAPBPYSPIC3XKVW5P4T45A","target":"record","payload":{"canonical_record":{"source":{"id":"1405.3319","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-05-13T22:31:09Z","cross_cats_sorted":["stat.ME","stat.ML"],"title_canon_sha256":"a78077fa97c0379901abbd3d52e7105b1db8656aec434a0294e144fc0dc915e2","abstract_canon_sha256":"6412a10975e516bee0889351a08c72c4218a7d8d108b4ef9aaf3b67a0239bdf5"},"schema_version":"1.0"},"canonical_sha256":"b7b10281e17e24f40b77556dd7f27ce812cc8b28b9de2c3c8e8cd170997f27da","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:10:24.815324Z","signature_b64":"m4X5tLWzzEpfvtCFTQijelHRa0ICxZM3WqL657CZ0S4wsbNGrkJ7gYyXmjDOGwQt1Fkc911qGKdf9+bUkmC/Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b7b10281e17e24f40b77556dd7f27ce812cc8b28b9de2c3c8e8cd170997f27da","last_reissued_at":"2026-05-18T00:10:24.814694Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:10:24.814694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1405.3319","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T00:10:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Wq1jg4YM7mMziZ8sZmZxT3uHPqJvbBQBPbr4WgndMIUmEFrK373uaZnJUGnBzSLYT//VYkW62AVETq4GZjOmBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T12:51:52.739778Z"},"content_sha256":"298120dc3ad5b00744e8009f7729a904c79d7db89e475c5fc269b10c76f2a7f4","schema_version":"1.0","event_id":"sha256:298120dc3ad5b00744e8009f7729a904c79d7db89e475c5fc269b10c76f2a7f4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2014:W6YQFAPBPYSPIC3XKVW5P4T45A","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Fully Bayesian Logistic Regression with Hyper-Lasso Priors for High-dimensional Feature Selection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.ML"],"primary_cat":"stat.CO","authors_text":"Longhai Li, Weixin Yao","submitted_at":"2014-05-13T22:31:09Z","abstract_excerpt":"High-dimensional feature selection arises in many areas of modern science. For example, in genomic research we want to find the genes that can be used to separate tissues of different classes (e.g. cancer and normal) from tens of thousands of genes that are active (expressed) in certain tissue cells. To this end, we wish to fit regression and classification models with a large number of features (also called variables, predictors). In the past decade, penalized likelihood methods for fitting regression models based on hyper-LASSO penalization have received increasing attention in the literatur"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1405.3319","kind":"arxiv","version":4},"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":"2026-05-18T00:10:24Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"liLo6Fz+ceC1rti31xDf03SHNZKRuhcGODcJ2CM5xjfwV5yPYiZ6Oue2cYdeBEOn+hfYrn85ZDc6qH3wlB+zCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-06T12:51:52.740121Z"},"content_sha256":"6b27da0e7d6539a1ff46f5065050c590671fadb3054a84fff85647df9be88f26","schema_version":"1.0","event_id":"sha256:6b27da0e7d6539a1ff46f5065050c590671fadb3054a84fff85647df9be88f26"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/W6YQFAPBPYSPIC3XKVW5P4T45A/bundle.json","state_url":"https://pith.science/pith/W6YQFAPBPYSPIC3XKVW5P4T45A/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/W6YQFAPBPYSPIC3XKVW5P4T45A/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":"2026-06-06T12:51:52Z","links":{"resolver":"https://pith.science/pith/W6YQFAPBPYSPIC3XKVW5P4T45A","bundle":"https://pith.science/pith/W6YQFAPBPYSPIC3XKVW5P4T45A/bundle.json","state":"https://pith.science/pith/W6YQFAPBPYSPIC3XKVW5P4T45A/state.json","well_known_bundle":"https://pith.science/.well-known/pith/W6YQFAPBPYSPIC3XKVW5P4T45A/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2014:W6YQFAPBPYSPIC3XKVW5P4T45A","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":"6412a10975e516bee0889351a08c72c4218a7d8d108b4ef9aaf3b67a0239bdf5","cross_cats_sorted":["stat.ME","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-05-13T22:31:09Z","title_canon_sha256":"a78077fa97c0379901abbd3d52e7105b1db8656aec434a0294e144fc0dc915e2"},"schema_version":"1.0","source":{"id":"1405.3319","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1405.3319","created_at":"2026-05-18T00:10:24Z"},{"alias_kind":"arxiv_version","alias_value":"1405.3319v4","created_at":"2026-05-18T00:10:24Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1405.3319","created_at":"2026-05-18T00:10:24Z"},{"alias_kind":"pith_short_12","alias_value":"W6YQFAPBPYSP","created_at":"2026-05-18T12:28:54Z"},{"alias_kind":"pith_short_16","alias_value":"W6YQFAPBPYSPIC3X","created_at":"2026-05-18T12:28:54Z"},{"alias_kind":"pith_short_8","alias_value":"W6YQFAPB","created_at":"2026-05-18T12:28:54Z"}],"graph_snapshots":[{"event_id":"sha256:6b27da0e7d6539a1ff46f5065050c590671fadb3054a84fff85647df9be88f26","target":"graph","created_at":"2026-05-18T00:10:24Z","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":"High-dimensional feature selection arises in many areas of modern science. For example, in genomic research we want to find the genes that can be used to separate tissues of different classes (e.g. cancer and normal) from tens of thousands of genes that are active (expressed) in certain tissue cells. To this end, we wish to fit regression and classification models with a large number of features (also called variables, predictors). In the past decade, penalized likelihood methods for fitting regression models based on hyper-LASSO penalization have received increasing attention in the literatur","authors_text":"Longhai Li, Weixin Yao","cross_cats":["stat.ME","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-05-13T22:31:09Z","title":"Fully Bayesian Logistic Regression with Hyper-Lasso Priors for High-dimensional Feature Selection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1405.3319","kind":"arxiv","version":4},"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":"sha256:298120dc3ad5b00744e8009f7729a904c79d7db89e475c5fc269b10c76f2a7f4","target":"record","created_at":"2026-05-18T00:10:24Z","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":"6412a10975e516bee0889351a08c72c4218a7d8d108b4ef9aaf3b67a0239bdf5","cross_cats_sorted":["stat.ME","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2014-05-13T22:31:09Z","title_canon_sha256":"a78077fa97c0379901abbd3d52e7105b1db8656aec434a0294e144fc0dc915e2"},"schema_version":"1.0","source":{"id":"1405.3319","kind":"arxiv","version":4}},"canonical_sha256":"b7b10281e17e24f40b77556dd7f27ce812cc8b28b9de2c3c8e8cd170997f27da","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b7b10281e17e24f40b77556dd7f27ce812cc8b28b9de2c3c8e8cd170997f27da","first_computed_at":"2026-05-18T00:10:24.814694Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:10:24.814694Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"m4X5tLWzzEpfvtCFTQijelHRa0ICxZM3WqL657CZ0S4wsbNGrkJ7gYyXmjDOGwQt1Fkc911qGKdf9+bUkmC/Dg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:10:24.815324Z","signed_message":"canonical_sha256_bytes"},"source_id":"1405.3319","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:298120dc3ad5b00744e8009f7729a904c79d7db89e475c5fc269b10c76f2a7f4","sha256:6b27da0e7d6539a1ff46f5065050c590671fadb3054a84fff85647df9be88f26"],"state_sha256":"25105ff326b30fd3dd4fe0bf41ae9b50563324f8600a02e033f664d7bf95f50a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"6bKtgqdiqO2BW/zlspceFL9g97hdJLWjcyVjNXPX4P66qb47Nj7qCjRPMO/zXXJGhou9PckAsV3YhWY/QjDJBg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-06T12:51:52.742085Z","bundle_sha256":"ee1d6c6bbc591575347cc18cdf7fef98e2dd51a57af78e2b8e2fa59baa883b83"}}