{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:ZAAYDZONE6XUEMBD36QXXJEOTN","short_pith_number":"pith:ZAAYDZON","canonical_record":{"source":{"id":"1802.02251","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-02-06T22:45:38Z","cross_cats_sorted":[],"title_canon_sha256":"901cb48e6c1701cfac5700fc054270cd13549a78e24702aa07e0fdb967ec3e41","abstract_canon_sha256":"a3792eefaf7e53a5f0d52019cdb0e8af0716eb06b48a7651a6d6ee936bc53b66"},"schema_version":"1.0"},"canonical_sha256":"c80181e5cd27af423023dfa17ba48e9b7d691bc331b766b868546e76c1efd55c","source":{"kind":"arxiv","id":"1802.02251","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.02251","created_at":"2026-05-18T00:24:08Z"},{"alias_kind":"arxiv_version","alias_value":"1802.02251v1","created_at":"2026-05-18T00:24:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.02251","created_at":"2026-05-18T00:24:08Z"},{"alias_kind":"pith_short_12","alias_value":"ZAAYDZONE6XU","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"ZAAYDZONE6XUEMBD","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"ZAAYDZON","created_at":"2026-05-18T12:33:04Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:ZAAYDZONE6XUEMBD36QXXJEOTN","target":"record","payload":{"canonical_record":{"source":{"id":"1802.02251","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-02-06T22:45:38Z","cross_cats_sorted":[],"title_canon_sha256":"901cb48e6c1701cfac5700fc054270cd13549a78e24702aa07e0fdb967ec3e41","abstract_canon_sha256":"a3792eefaf7e53a5f0d52019cdb0e8af0716eb06b48a7651a6d6ee936bc53b66"},"schema_version":"1.0"},"canonical_sha256":"c80181e5cd27af423023dfa17ba48e9b7d691bc331b766b868546e76c1efd55c","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:24:08.774103Z","signature_b64":"04+OCfXntc9M2pHTQFWXsXpwlJKReoDUAegCNaqDd8LboRulSl8lZqSQ9XmGSB/SPpfPIYb1eEmOEGPOM7DVAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c80181e5cd27af423023dfa17ba48e9b7d691bc331b766b868546e76c1efd55c","last_reissued_at":"2026-05-18T00:24:08.773475Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:24:08.773475Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1802.02251","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":"2026-05-18T00:24:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ifUAMAQrU4rVP8XDSmbxAC+KduITOCU1WMyBqkHp0TGAyaZRbMorFvde9crp4B78/7XDRDp1H7fM10ca98t/Cg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-26T05:26:00.321521Z"},"content_sha256":"8878ff207070317df68c61a82242f046ad8b45b15a93fe9468331dd5541f2f38","schema_version":"1.0","event_id":"sha256:8878ff207070317df68c61a82242f046ad8b45b15a93fe9468331dd5541f2f38"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:ZAAYDZONE6XUEMBD36QXXJEOTN","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"An Imputation-Consistency Algorithm for High-Dimensional Missing Data Problems and Beyond","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Bochao Jia, Faming Liang, Jingnan Xue, Qizhai Li, Ye Luo","submitted_at":"2018-02-06T22:45:38Z","abstract_excerpt":"Missing data are frequently encountered in high-dimensional problems, but they are usually difficult to deal with using standard algorithms, such as the expectation-maximization (EM) algorithm and its variants. To tackle this difficulty, some problem-specific algorithms have been developed in the literature, but there still lacks a general algorithm. This work is to fill the gap: we propose a general algorithm for high-dimensional missing data problems. The proposed algorithm works by iterating between an imputation step and a consistency step. At the imputation step, the missing data are impu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.02251","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":"2026-05-18T00:24:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"pVS2TG4tR1tyUDEc6EDvRKfGF9yOTh6AdjsVGFxX6gNyl20QeNFNZ3U8W6/rxkLsXi6+m65Q59yRQCmVFrUrBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-26T05:26:00.321897Z"},"content_sha256":"e311962bc3528368cff72e5b7a113d3bb161024411c58a4a0957e2937f5e60ee","schema_version":"1.0","event_id":"sha256:e311962bc3528368cff72e5b7a113d3bb161024411c58a4a0957e2937f5e60ee"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZAAYDZONE6XUEMBD36QXXJEOTN/bundle.json","state_url":"https://pith.science/pith/ZAAYDZONE6XUEMBD36QXXJEOTN/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZAAYDZONE6XUEMBD36QXXJEOTN/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":"2026-06-26T05:26:00Z","links":{"resolver":"https://pith.science/pith/ZAAYDZONE6XUEMBD36QXXJEOTN","bundle":"https://pith.science/pith/ZAAYDZONE6XUEMBD36QXXJEOTN/bundle.json","state":"https://pith.science/pith/ZAAYDZONE6XUEMBD36QXXJEOTN/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZAAYDZONE6XUEMBD36QXXJEOTN/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:ZAAYDZONE6XUEMBD36QXXJEOTN","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":"a3792eefaf7e53a5f0d52019cdb0e8af0716eb06b48a7651a6d6ee936bc53b66","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-02-06T22:45:38Z","title_canon_sha256":"901cb48e6c1701cfac5700fc054270cd13549a78e24702aa07e0fdb967ec3e41"},"schema_version":"1.0","source":{"id":"1802.02251","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1802.02251","created_at":"2026-05-18T00:24:08Z"},{"alias_kind":"arxiv_version","alias_value":"1802.02251v1","created_at":"2026-05-18T00:24:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1802.02251","created_at":"2026-05-18T00:24:08Z"},{"alias_kind":"pith_short_12","alias_value":"ZAAYDZONE6XU","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_16","alias_value":"ZAAYDZONE6XUEMBD","created_at":"2026-05-18T12:33:04Z"},{"alias_kind":"pith_short_8","alias_value":"ZAAYDZON","created_at":"2026-05-18T12:33:04Z"}],"graph_snapshots":[{"event_id":"sha256:e311962bc3528368cff72e5b7a113d3bb161024411c58a4a0957e2937f5e60ee","target":"graph","created_at":"2026-05-18T00:24:08Z","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":"Missing data are frequently encountered in high-dimensional problems, but they are usually difficult to deal with using standard algorithms, such as the expectation-maximization (EM) algorithm and its variants. To tackle this difficulty, some problem-specific algorithms have been developed in the literature, but there still lacks a general algorithm. This work is to fill the gap: we propose a general algorithm for high-dimensional missing data problems. The proposed algorithm works by iterating between an imputation step and a consistency step. At the imputation step, the missing data are impu","authors_text":"Bochao Jia, Faming Liang, Jingnan Xue, Qizhai Li, Ye Luo","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-02-06T22:45:38Z","title":"An Imputation-Consistency Algorithm for High-Dimensional Missing Data Problems and Beyond"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1802.02251","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":"sha256:8878ff207070317df68c61a82242f046ad8b45b15a93fe9468331dd5541f2f38","target":"record","created_at":"2026-05-18T00:24:08Z","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":"a3792eefaf7e53a5f0d52019cdb0e8af0716eb06b48a7651a6d6ee936bc53b66","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-02-06T22:45:38Z","title_canon_sha256":"901cb48e6c1701cfac5700fc054270cd13549a78e24702aa07e0fdb967ec3e41"},"schema_version":"1.0","source":{"id":"1802.02251","kind":"arxiv","version":1}},"canonical_sha256":"c80181e5cd27af423023dfa17ba48e9b7d691bc331b766b868546e76c1efd55c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c80181e5cd27af423023dfa17ba48e9b7d691bc331b766b868546e76c1efd55c","first_computed_at":"2026-05-18T00:24:08.773475Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:24:08.773475Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"04+OCfXntc9M2pHTQFWXsXpwlJKReoDUAegCNaqDd8LboRulSl8lZqSQ9XmGSB/SPpfPIYb1eEmOEGPOM7DVAg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:24:08.774103Z","signed_message":"canonical_sha256_bytes"},"source_id":"1802.02251","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8878ff207070317df68c61a82242f046ad8b45b15a93fe9468331dd5541f2f38","sha256:e311962bc3528368cff72e5b7a113d3bb161024411c58a4a0957e2937f5e60ee"],"state_sha256":"0961881a44df07e16fb31f8e779c4151c5e76d37b5c65c4ec0d31da96e92a34c"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yQLhELIXtsai693ov+wOQpyIWr6RNk22ob84224QcyafDlAPgXc9DuLuUPJ3psk1MjixICoZ1kxy0UWqlNaUBQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-26T05:26:00.323843Z","bundle_sha256":"0e236354a7ec0e818a823c9187624d824eb0c7be70c0586d17b53ff56677d722"}}