{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2015:UZQFMLOD6BKS6SUNLECLRBMNCE","short_pith_number":"pith:UZQFMLOD","canonical_record":{"source":{"id":"1510.03781","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-10-13T17:09:04Z","cross_cats_sorted":[],"title_canon_sha256":"76523ce7b367d766bb7b61aa22deecb80d1ddc6187967312bf7f48181998fcc7","abstract_canon_sha256":"7c41ca3f43f54436513feaf64f6adbcf9cbdb149f89f3ba186899959a902d239"},"schema_version":"1.0"},"canonical_sha256":"a660562dc3f0552f4a8d5904b8858d112bfeb93273455acc78512a19bfa631a0","source":{"kind":"arxiv","id":"1510.03781","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1510.03781","created_at":"2026-05-18T01:30:16Z"},{"alias_kind":"arxiv_version","alias_value":"1510.03781v1","created_at":"2026-05-18T01:30:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.03781","created_at":"2026-05-18T01:30:16Z"},{"alias_kind":"pith_short_12","alias_value":"UZQFMLOD6BKS","created_at":"2026-05-18T12:29:44Z"},{"alias_kind":"pith_short_16","alias_value":"UZQFMLOD6BKS6SUN","created_at":"2026-05-18T12:29:44Z"},{"alias_kind":"pith_short_8","alias_value":"UZQFMLOD","created_at":"2026-05-18T12:29:44Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2015:UZQFMLOD6BKS6SUNLECLRBMNCE","target":"record","payload":{"canonical_record":{"source":{"id":"1510.03781","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-10-13T17:09:04Z","cross_cats_sorted":[],"title_canon_sha256":"76523ce7b367d766bb7b61aa22deecb80d1ddc6187967312bf7f48181998fcc7","abstract_canon_sha256":"7c41ca3f43f54436513feaf64f6adbcf9cbdb149f89f3ba186899959a902d239"},"schema_version":"1.0"},"canonical_sha256":"a660562dc3f0552f4a8d5904b8858d112bfeb93273455acc78512a19bfa631a0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:30:16.179387Z","signature_b64":"gYeyeon7ZRf7I0SjduVYRoV3/WaPl8mu3mKGvWIw21+XV6E3uuEeBFE2HXVipKpUauVMVlHz8Pu2pZdo+o8xDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a660562dc3f0552f4a8d5904b8858d112bfeb93273455acc78512a19bfa631a0","last_reissued_at":"2026-05-18T01:30:16.178803Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:30:16.178803Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1510.03781","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":"2026-05-18T01:30:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"57b0PaqtX0WVYvC4DajawLF/yPgBLDbovY5nMTqzR8hROPn+zEdzmsAVyxLKNGfemEnDO0zawvfiP5FTNmEvDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T03:08:14.260085Z"},"content_sha256":"2b25a5dcf9fb187cf605c42cdaa6eef797453ac73fbadee68cab3f0ddc2c1b64","schema_version":"1.0","event_id":"sha256:2b25a5dcf9fb187cf605c42cdaa6eef797453ac73fbadee68cab3f0ddc2c1b64"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2015:UZQFMLOD6BKS6SUNLECLRBMNCE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Scalable Empirical Bayes Approach to Variable Selection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Haim Y. Bar, James G. Booth, Martin T. Wells","submitted_at":"2015-10-13T17:09:04Z","abstract_excerpt":"We develop a model-based empirical Bayes approach to variable selection problems in which the number of predictors is very large, possibly much larger than the number of responses (the so-called 'large p, small n' problem). We consider the multiple linear regression setting, where the response is assumed to be a continuous variable and it is a linear function of the predictors plus error. The explanatory variables in the linear model can have a positive effect on the response, a negative effect, or no effect. We model the effects of the linear predictors as a three-component mixture in which a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.03781","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":"2026-05-18T01:30:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"bFyjvly94QNPQXmkFO2gxRiDAxeuHcBKHUJygkdBzejcRCWa06nmF4VcvUWv441gXtU2R8GBOhXeJk/hltpsDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-20T03:08:14.260788Z"},"content_sha256":"65e899cc67675ef304daae51d65377df0e5251c4e2d2aa6c8924a227eda0fae4","schema_version":"1.0","event_id":"sha256:65e899cc67675ef304daae51d65377df0e5251c4e2d2aa6c8924a227eda0fae4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UZQFMLOD6BKS6SUNLECLRBMNCE/bundle.json","state_url":"https://pith.science/pith/UZQFMLOD6BKS6SUNLECLRBMNCE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UZQFMLOD6BKS6SUNLECLRBMNCE/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":"2026-05-20T03:08:14Z","links":{"resolver":"https://pith.science/pith/UZQFMLOD6BKS6SUNLECLRBMNCE","bundle":"https://pith.science/pith/UZQFMLOD6BKS6SUNLECLRBMNCE/bundle.json","state":"https://pith.science/pith/UZQFMLOD6BKS6SUNLECLRBMNCE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UZQFMLOD6BKS6SUNLECLRBMNCE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2015:UZQFMLOD6BKS6SUNLECLRBMNCE","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":"7c41ca3f43f54436513feaf64f6adbcf9cbdb149f89f3ba186899959a902d239","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-10-13T17:09:04Z","title_canon_sha256":"76523ce7b367d766bb7b61aa22deecb80d1ddc6187967312bf7f48181998fcc7"},"schema_version":"1.0","source":{"id":"1510.03781","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1510.03781","created_at":"2026-05-18T01:30:16Z"},{"alias_kind":"arxiv_version","alias_value":"1510.03781v1","created_at":"2026-05-18T01:30:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1510.03781","created_at":"2026-05-18T01:30:16Z"},{"alias_kind":"pith_short_12","alias_value":"UZQFMLOD6BKS","created_at":"2026-05-18T12:29:44Z"},{"alias_kind":"pith_short_16","alias_value":"UZQFMLOD6BKS6SUN","created_at":"2026-05-18T12:29:44Z"},{"alias_kind":"pith_short_8","alias_value":"UZQFMLOD","created_at":"2026-05-18T12:29:44Z"}],"graph_snapshots":[{"event_id":"sha256:65e899cc67675ef304daae51d65377df0e5251c4e2d2aa6c8924a227eda0fae4","target":"graph","created_at":"2026-05-18T01:30:16Z","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 develop a model-based empirical Bayes approach to variable selection problems in which the number of predictors is very large, possibly much larger than the number of responses (the so-called 'large p, small n' problem). We consider the multiple linear regression setting, where the response is assumed to be a continuous variable and it is a linear function of the predictors plus error. The explanatory variables in the linear model can have a positive effect on the response, a negative effect, or no effect. We model the effects of the linear predictors as a three-component mixture in which a","authors_text":"Haim Y. Bar, James G. Booth, Martin T. Wells","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-10-13T17:09:04Z","title":"A Scalable Empirical Bayes Approach to Variable Selection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1510.03781","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":"sha256:2b25a5dcf9fb187cf605c42cdaa6eef797453ac73fbadee68cab3f0ddc2c1b64","target":"record","created_at":"2026-05-18T01:30:16Z","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":"7c41ca3f43f54436513feaf64f6adbcf9cbdb149f89f3ba186899959a902d239","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-10-13T17:09:04Z","title_canon_sha256":"76523ce7b367d766bb7b61aa22deecb80d1ddc6187967312bf7f48181998fcc7"},"schema_version":"1.0","source":{"id":"1510.03781","kind":"arxiv","version":1}},"canonical_sha256":"a660562dc3f0552f4a8d5904b8858d112bfeb93273455acc78512a19bfa631a0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a660562dc3f0552f4a8d5904b8858d112bfeb93273455acc78512a19bfa631a0","first_computed_at":"2026-05-18T01:30:16.178803Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:30:16.178803Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"gYeyeon7ZRf7I0SjduVYRoV3/WaPl8mu3mKGvWIw21+XV6E3uuEeBFE2HXVipKpUauVMVlHz8Pu2pZdo+o8xDQ==","signature_status":"signed_v1","signed_at":"2026-05-18T01:30:16.179387Z","signed_message":"canonical_sha256_bytes"},"source_id":"1510.03781","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2b25a5dcf9fb187cf605c42cdaa6eef797453ac73fbadee68cab3f0ddc2c1b64","sha256:65e899cc67675ef304daae51d65377df0e5251c4e2d2aa6c8924a227eda0fae4"],"state_sha256":"62362b201f04aed2bf2e18c0dcbf4e0f2717f603bbfc34cce8606b594d3c6511"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"b2DKInhrfsPCW2f+yRpBB6iDcOhf6wp3KMPtjx4fbBDxgNmRcR+EGvaqYrji0K+uWRyb5Mdg7YieKmTJop/LDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-20T03:08:14.264962Z","bundle_sha256":"be811dcdeb4f8d5b7428284654ebf9507e11293aa7a8804c46dad9c1706a0c7f"}}