{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:F434NFIUHS3Y5POVF4SXAKATP2","short_pith_number":"pith:F434NFIU","canonical_record":{"source":{"id":"1702.02686","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-02-09T03:10:13Z","cross_cats_sorted":["cs.LG","stat.ME"],"title_canon_sha256":"6e7543f622d0c17fb57c652e2660d482f847a1f5d0f04db5ff1c425537ee674e","abstract_canon_sha256":"59340f5612ace2708808ba89a3779bf59b18ad525558d5f32dd8e26271408296"},"schema_version":"1.0"},"canonical_sha256":"2f37c695143cb78ebdd52f257028137e90466fe2e0051e4b301ffbd0bc0979cf","source":{"kind":"arxiv","id":"1702.02686","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.02686","created_at":"2026-05-18T00:31:28Z"},{"alias_kind":"arxiv_version","alias_value":"1702.02686v2","created_at":"2026-05-18T00:31:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.02686","created_at":"2026-05-18T00:31:28Z"},{"alias_kind":"pith_short_12","alias_value":"F434NFIUHS3Y","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"F434NFIUHS3Y5POV","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"F434NFIU","created_at":"2026-05-18T12:31:15Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:F434NFIUHS3Y5POVF4SXAKATP2","target":"record","payload":{"canonical_record":{"source":{"id":"1702.02686","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-02-09T03:10:13Z","cross_cats_sorted":["cs.LG","stat.ME"],"title_canon_sha256":"6e7543f622d0c17fb57c652e2660d482f847a1f5d0f04db5ff1c425537ee674e","abstract_canon_sha256":"59340f5612ace2708808ba89a3779bf59b18ad525558d5f32dd8e26271408296"},"schema_version":"1.0"},"canonical_sha256":"2f37c695143cb78ebdd52f257028137e90466fe2e0051e4b301ffbd0bc0979cf","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:31:28.926457Z","signature_b64":"WCL8DhQGd+R88boUzYyBa6ijhph1Mip6hfZDCTKh2HJB9G+SglnHH7STtb72npppv31Na9i2G7uYVR3vtGyqBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2f37c695143cb78ebdd52f257028137e90466fe2e0051e4b301ffbd0bc0979cf","last_reissued_at":"2026-05-18T00:31:28.925903Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:31:28.925903Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1702.02686","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":"2026-05-18T00:31:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"QAZud2FomZMiESLGQLWIJJL0IHvx6HryNMjf9CvpVB139E01yuqSeuYBNRrirj2rLPdljuUJHygQXNBcxLTgAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T14:33:37.609334Z"},"content_sha256":"d53d0b67b462de335fa0396d94ddfbcee8b598622f9f90e5ae54d59c3a08382f","schema_version":"1.0","event_id":"sha256:d53d0b67b462de335fa0396d94ddfbcee8b598622f9f90e5ae54d59c3a08382f"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:F434NFIUHS3Y5POVF4SXAKATP2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ME"],"primary_cat":"stat.ML","authors_text":"Aarti Singh, Jialei Wang, Sivaraman Balakrishnan, Yining Wang","submitted_at":"2017-02-09T03:10:13Z","abstract_excerpt":"Although a majority of the theoretical literature in high-dimensional statistics has focused on settings which involve fully-observed data, settings with missing values and corruptions are common in practice. We consider the problems of estimation and of constructing component-wise confidence intervals in a sparse high-dimensional linear regression model when some covariates of the design matrix are missing completely at random. We analyze a variant of the Dantzig selector [9] for estimating the regression model and we use a de-biasing argument to construct component-wise confidence intervals."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.02686","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":"2026-05-18T00:31:28Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"URQaM6YsHS376ark+F3rNeYhb24dGimL9FZKyupJl5zym9d0DdhdajIuhFXSOLvtCalAptTSoVgbmvNszVqPDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T14:33:37.609716Z"},"content_sha256":"4a4b0f58c959fe3a0ecb57a1186e96d14ff5c2be98729a076bcd3f7833eecf01","schema_version":"1.0","event_id":"sha256:4a4b0f58c959fe3a0ecb57a1186e96d14ff5c2be98729a076bcd3f7833eecf01"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/F434NFIUHS3Y5POVF4SXAKATP2/bundle.json","state_url":"https://pith.science/pith/F434NFIUHS3Y5POVF4SXAKATP2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/F434NFIUHS3Y5POVF4SXAKATP2/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":"2026-06-02T14:33:37Z","links":{"resolver":"https://pith.science/pith/F434NFIUHS3Y5POVF4SXAKATP2","bundle":"https://pith.science/pith/F434NFIUHS3Y5POVF4SXAKATP2/bundle.json","state":"https://pith.science/pith/F434NFIUHS3Y5POVF4SXAKATP2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/F434NFIUHS3Y5POVF4SXAKATP2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:F434NFIUHS3Y5POVF4SXAKATP2","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":"59340f5612ace2708808ba89a3779bf59b18ad525558d5f32dd8e26271408296","cross_cats_sorted":["cs.LG","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-02-09T03:10:13Z","title_canon_sha256":"6e7543f622d0c17fb57c652e2660d482f847a1f5d0f04db5ff1c425537ee674e"},"schema_version":"1.0","source":{"id":"1702.02686","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1702.02686","created_at":"2026-05-18T00:31:28Z"},{"alias_kind":"arxiv_version","alias_value":"1702.02686v2","created_at":"2026-05-18T00:31:28Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1702.02686","created_at":"2026-05-18T00:31:28Z"},{"alias_kind":"pith_short_12","alias_value":"F434NFIUHS3Y","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_16","alias_value":"F434NFIUHS3Y5POV","created_at":"2026-05-18T12:31:15Z"},{"alias_kind":"pith_short_8","alias_value":"F434NFIU","created_at":"2026-05-18T12:31:15Z"}],"graph_snapshots":[{"event_id":"sha256:4a4b0f58c959fe3a0ecb57a1186e96d14ff5c2be98729a076bcd3f7833eecf01","target":"graph","created_at":"2026-05-18T00:31:28Z","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":"Although a majority of the theoretical literature in high-dimensional statistics has focused on settings which involve fully-observed data, settings with missing values and corruptions are common in practice. We consider the problems of estimation and of constructing component-wise confidence intervals in a sparse high-dimensional linear regression model when some covariates of the design matrix are missing completely at random. We analyze a variant of the Dantzig selector [9] for estimating the regression model and we use a de-biasing argument to construct component-wise confidence intervals.","authors_text":"Aarti Singh, Jialei Wang, Sivaraman Balakrishnan, Yining Wang","cross_cats":["cs.LG","stat.ME"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-02-09T03:10:13Z","title":"Rate Optimal Estimation and Confidence Intervals for High-dimensional Regression with Missing Covariates"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1702.02686","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:d53d0b67b462de335fa0396d94ddfbcee8b598622f9f90e5ae54d59c3a08382f","target":"record","created_at":"2026-05-18T00:31:28Z","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":"59340f5612ace2708808ba89a3779bf59b18ad525558d5f32dd8e26271408296","cross_cats_sorted":["cs.LG","stat.ME"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2017-02-09T03:10:13Z","title_canon_sha256":"6e7543f622d0c17fb57c652e2660d482f847a1f5d0f04db5ff1c425537ee674e"},"schema_version":"1.0","source":{"id":"1702.02686","kind":"arxiv","version":2}},"canonical_sha256":"2f37c695143cb78ebdd52f257028137e90466fe2e0051e4b301ffbd0bc0979cf","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"2f37c695143cb78ebdd52f257028137e90466fe2e0051e4b301ffbd0bc0979cf","first_computed_at":"2026-05-18T00:31:28.925903Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:31:28.925903Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"WCL8DhQGd+R88boUzYyBa6ijhph1Mip6hfZDCTKh2HJB9G+SglnHH7STtb72npppv31Na9i2G7uYVR3vtGyqBQ==","signature_status":"signed_v1","signed_at":"2026-05-18T00:31:28.926457Z","signed_message":"canonical_sha256_bytes"},"source_id":"1702.02686","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d53d0b67b462de335fa0396d94ddfbcee8b598622f9f90e5ae54d59c3a08382f","sha256:4a4b0f58c959fe3a0ecb57a1186e96d14ff5c2be98729a076bcd3f7833eecf01"],"state_sha256":"049aa663f1b0cc4d8ee8a164750240c6f58fbcf8d88bdc3b26f082ad9c8fc2ad"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OMw1Ez5Dkn4alodVNAHQ7yM4yGEjqRbC21sdT6tuDwbjcSLopxiSVUHvM8XthOxLxdIVLaYzr/GTJX4cgXphAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T14:33:37.611646Z","bundle_sha256":"c2a013f909f0a2824d3ac6030872e199c4a8aea0b9caf71c21656136856187ec"}}