{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:CODUGXXEC5YKTPSAZH4CTTEWJ5","short_pith_number":"pith:CODUGXXE","canonical_record":{"source":{"id":"2201.10208","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2022-01-25T10:02:23Z","cross_cats_sorted":["math.ST","stat.ML","stat.TH"],"title_canon_sha256":"044a6b925c4503afa6e0b48058138c4af5c90994543beb7f4d0e4035ca92006d","abstract_canon_sha256":"5eaf9091210ce994187f68100c873b22cdbcce1f8d7d10cf0d07d57e8ef23a02"},"schema_version":"1.0"},"canonical_sha256":"1387435ee41770a9be40c9f829cc964f4662011134642090b0611897fa9231d3","source":{"kind":"arxiv","id":"2201.10208","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2201.10208","created_at":"2026-07-05T08:55:19Z"},{"alias_kind":"arxiv_version","alias_value":"2201.10208v2","created_at":"2026-07-05T08:55:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.10208","created_at":"2026-07-05T08:55:19Z"},{"alias_kind":"pith_short_12","alias_value":"CODUGXXEC5YK","created_at":"2026-07-05T08:55:19Z"},{"alias_kind":"pith_short_16","alias_value":"CODUGXXEC5YKTPSA","created_at":"2026-07-05T08:55:19Z"},{"alias_kind":"pith_short_8","alias_value":"CODUGXXE","created_at":"2026-07-05T08:55:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:CODUGXXEC5YKTPSAZH4CTTEWJ5","target":"record","payload":{"canonical_record":{"source":{"id":"2201.10208","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2022-01-25T10:02:23Z","cross_cats_sorted":["math.ST","stat.ML","stat.TH"],"title_canon_sha256":"044a6b925c4503afa6e0b48058138c4af5c90994543beb7f4d0e4035ca92006d","abstract_canon_sha256":"5eaf9091210ce994187f68100c873b22cdbcce1f8d7d10cf0d07d57e8ef23a02"},"schema_version":"1.0"},"canonical_sha256":"1387435ee41770a9be40c9f829cc964f4662011134642090b0611897fa9231d3","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:55:19.026800Z","signature_b64":"tQocB183nP5eEjoABBJfS20h/n76KK0Jf+l9Wv/OqyF/P1d9UWlP6Rg5b4661RIGEeMRVLLLHwZHMS2eMliBDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1387435ee41770a9be40c9f829cc964f4662011134642090b0611897fa9231d3","last_reissued_at":"2026-07-05T08:55:19.026299Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:55:19.026299Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2201.10208","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":"2026-07-05T08:55:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8XzgEngpO2tE+C6FMhcETX1lLotwfx9/W+VhLbES0iiubu2aCwl9CrKf8rotpzQO145nZYtD68W5qw18HZwfAw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T16:19:50.593548Z"},"content_sha256":"9072a1c0e5b0d57241a5cdbcc9a04b031cba94975d30c50cf5612855a16eeb70","schema_version":"1.0","event_id":"sha256:9072a1c0e5b0d57241a5cdbcc9a04b031cba94975d30c50cf5612855a16eeb70"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:CODUGXXEC5YKTPSAZH4CTTEWJ5","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Semi-Supervised Quantile Estimation: Robust and Efficient Inference in High Dimensional Settings","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.ML","stat.TH"],"primary_cat":"stat.ME","authors_text":"Abhishek Chakrabortty, Guorong Dai, Raymond J. Carroll","submitted_at":"2022-01-25T10:02:23Z","abstract_excerpt":"We consider quantile estimation in a semi-supervised setting, characterized by two available data sets: (i) a small or moderate sized labeled data set containing observations for a response and a set of possibly high dimensional covariates, and (ii) a much larger unlabeled data set where only the covariates are observed. We propose a family of semi-supervised estimators for the response quantile(s) based on the two data sets, to improve the estimation accuracy compared to the supervised estimator, i.e., the sample quantile from the labeled data. These estimators use a flexible imputation strat"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.10208","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2201.10208/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2026-07-05T08:55:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"YF2fWv7mcR9+5vYjztBHUnsXPNI1XWUtesGO3qd/QR/nxMKef2xV8+Wo/5rCVbwYqTxp6TtIWeCT8JZGKKXwDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-08T16:19:50.594285Z"},"content_sha256":"46a034f2d0ea6667a4cb44b837d2d56e8412eb3b750305d72200ef01c1e5e7b3","schema_version":"1.0","event_id":"sha256:46a034f2d0ea6667a4cb44b837d2d56e8412eb3b750305d72200ef01c1e5e7b3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/CODUGXXEC5YKTPSAZH4CTTEWJ5/bundle.json","state_url":"https://pith.science/pith/CODUGXXEC5YKTPSAZH4CTTEWJ5/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/CODUGXXEC5YKTPSAZH4CTTEWJ5/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":"2026-08-08T16:19:50Z","links":{"resolver":"https://pith.science/pith/CODUGXXEC5YKTPSAZH4CTTEWJ5","bundle":"https://pith.science/pith/CODUGXXEC5YKTPSAZH4CTTEWJ5/bundle.json","state":"https://pith.science/pith/CODUGXXEC5YKTPSAZH4CTTEWJ5/state.json","well_known_bundle":"https://pith.science/.well-known/pith/CODUGXXEC5YKTPSAZH4CTTEWJ5/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:CODUGXXEC5YKTPSAZH4CTTEWJ5","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":"5eaf9091210ce994187f68100c873b22cdbcce1f8d7d10cf0d07d57e8ef23a02","cross_cats_sorted":["math.ST","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2022-01-25T10:02:23Z","title_canon_sha256":"044a6b925c4503afa6e0b48058138c4af5c90994543beb7f4d0e4035ca92006d"},"schema_version":"1.0","source":{"id":"2201.10208","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2201.10208","created_at":"2026-07-05T08:55:19Z"},{"alias_kind":"arxiv_version","alias_value":"2201.10208v2","created_at":"2026-07-05T08:55:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2201.10208","created_at":"2026-07-05T08:55:19Z"},{"alias_kind":"pith_short_12","alias_value":"CODUGXXEC5YK","created_at":"2026-07-05T08:55:19Z"},{"alias_kind":"pith_short_16","alias_value":"CODUGXXEC5YKTPSA","created_at":"2026-07-05T08:55:19Z"},{"alias_kind":"pith_short_8","alias_value":"CODUGXXE","created_at":"2026-07-05T08:55:19Z"}],"graph_snapshots":[{"event_id":"sha256:46a034f2d0ea6667a4cb44b837d2d56e8412eb3b750305d72200ef01c1e5e7b3","target":"graph","created_at":"2026-07-05T08:55:19Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2201.10208/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We consider quantile estimation in a semi-supervised setting, characterized by two available data sets: (i) a small or moderate sized labeled data set containing observations for a response and a set of possibly high dimensional covariates, and (ii) a much larger unlabeled data set where only the covariates are observed. We propose a family of semi-supervised estimators for the response quantile(s) based on the two data sets, to improve the estimation accuracy compared to the supervised estimator, i.e., the sample quantile from the labeled data. These estimators use a flexible imputation strat","authors_text":"Abhishek Chakrabortty, Guorong Dai, Raymond J. Carroll","cross_cats":["math.ST","stat.ML","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2022-01-25T10:02:23Z","title":"Semi-Supervised Quantile Estimation: Robust and Efficient Inference in High Dimensional Settings"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2201.10208","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":"sha256:9072a1c0e5b0d57241a5cdbcc9a04b031cba94975d30c50cf5612855a16eeb70","target":"record","created_at":"2026-07-05T08:55:19Z","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":"5eaf9091210ce994187f68100c873b22cdbcce1f8d7d10cf0d07d57e8ef23a02","cross_cats_sorted":["math.ST","stat.ML","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2022-01-25T10:02:23Z","title_canon_sha256":"044a6b925c4503afa6e0b48058138c4af5c90994543beb7f4d0e4035ca92006d"},"schema_version":"1.0","source":{"id":"2201.10208","kind":"arxiv","version":2}},"canonical_sha256":"1387435ee41770a9be40c9f829cc964f4662011134642090b0611897fa9231d3","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1387435ee41770a9be40c9f829cc964f4662011134642090b0611897fa9231d3","first_computed_at":"2026-07-05T08:55:19.026299Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:55:19.026299Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"tQocB183nP5eEjoABBJfS20h/n76KK0Jf+l9Wv/OqyF/P1d9UWlP6Rg5b4661RIGEeMRVLLLHwZHMS2eMliBDg==","signature_status":"signed_v1","signed_at":"2026-07-05T08:55:19.026800Z","signed_message":"canonical_sha256_bytes"},"source_id":"2201.10208","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9072a1c0e5b0d57241a5cdbcc9a04b031cba94975d30c50cf5612855a16eeb70","sha256:46a034f2d0ea6667a4cb44b837d2d56e8412eb3b750305d72200ef01c1e5e7b3"],"state_sha256":"db91337fb7c23dc693a2a2f12f964facb2ea3ff25391bfd6d88ba6264a40ff09"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZakGiDWryrQNKAkSZfaw8vyLYHm60pSF8c/TKJZKzP0MFJJlE4bSuHjS+UiFxjLm7uVHeH8cVNwiizVkPivWAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-08T16:19:50.600292Z","bundle_sha256":"03bc32254964b8f3e2d9e3424ff93fd6868acc4e376d7821ce037a61987e7730"}}