{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:4GOKJT5RFNJLVVWL6TAGDZLKMC","short_pith_number":"pith:4GOKJT5R","canonical_record":{"source":{"id":"2606.23363","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2026-06-22T14:00:11Z","cross_cats_sorted":[],"title_canon_sha256":"2d01f7061fe6da6cb4cb3050f891563a3c274d3cfc2315bffaf8ad403a602d51","abstract_canon_sha256":"3fddab77ed21fa78d62c0e3a2e4e7fc237604845e390808090701194473d7965"},"schema_version":"1.0"},"canonical_sha256":"e19ca4cfb12b52bad6cbf4c061e56a60aa2def86639e57735d7c9b460e89ad32","source":{"kind":"arxiv","id":"2606.23363","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.23363","created_at":"2026-06-23T03:14:17Z"},{"alias_kind":"arxiv_version","alias_value":"2606.23363v1","created_at":"2026-06-23T03:14:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.23363","created_at":"2026-06-23T03:14:17Z"},{"alias_kind":"pith_short_12","alias_value":"4GOKJT5RFNJL","created_at":"2026-06-23T03:14:17Z"},{"alias_kind":"pith_short_16","alias_value":"4GOKJT5RFNJLVVWL","created_at":"2026-06-23T03:14:17Z"},{"alias_kind":"pith_short_8","alias_value":"4GOKJT5R","created_at":"2026-06-23T03:14:17Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:4GOKJT5RFNJLVVWL6TAGDZLKMC","target":"record","payload":{"canonical_record":{"source":{"id":"2606.23363","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2026-06-22T14:00:11Z","cross_cats_sorted":[],"title_canon_sha256":"2d01f7061fe6da6cb4cb3050f891563a3c274d3cfc2315bffaf8ad403a602d51","abstract_canon_sha256":"3fddab77ed21fa78d62c0e3a2e4e7fc237604845e390808090701194473d7965"},"schema_version":"1.0"},"canonical_sha256":"e19ca4cfb12b52bad6cbf4c061e56a60aa2def86639e57735d7c9b460e89ad32","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T03:14:17.863449Z","signature_b64":"fRCn2qFi2v+eN6WxWsbhIB1Xa+xaj0ka1hmLAe/Y4MxEw3sKECarC+0bwYN+sY/ZfsKck/hBVYi4N7S82NC8AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e19ca4cfb12b52bad6cbf4c061e56a60aa2def86639e57735d7c9b460e89ad32","last_reissued_at":"2026-06-23T03:14:17.863030Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T03:14:17.863030Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.23363","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":"2026-06-23T03:14:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"5J0H6xgfyWj/5KduqcLZYsLNcAfdfXYuphwstdx4sz1fXfSwGR/r8+Vzo8Huub8BzZiWw2//olrUiMLhAdA4DA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T21:08:09.366905Z"},"content_sha256":"d280a8eb631f349ca9503484c3637fb4aca6e8d1bfdcfd36013921988cdcc75b","schema_version":"1.0","event_id":"sha256:d280a8eb631f349ca9503484c3637fb4aca6e8d1bfdcfd36013921988cdcc75b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:4GOKJT5RFNJLVVWL6TAGDZLKMC","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Optimal Poisson subsampling for quantile regression with large-scale longitudinal data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Chunjing Li, Jiahui Zhang, Xiaohui Yuan","submitted_at":"2026-06-22T14:00:11Z","abstract_excerpt":"To address the computational challenges arising from large-scale longitudinal data, an optimal Poisson subsampling algorithm is proposed for quantile regression. The proposed method can substantially alleviate computational burden. Under some regularity conditions, we derive the asymptotic properties of the estimators from weighted quantile generalized estimating equations. For practical implementation, an efficient algorithm is proposed for parameter estimation. Furthermore, asymptotic theory is established for penalized weighted smooth quantile generalized estimating equations, and regulariz"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.23363","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.23363/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":"2026-06-23T03:14:17Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"v6vNO4vcP6teAj7mP95OAZQQQtk/BTQOPcF7vIvmg7+cbxL85WBqJU1sf6HnjpcA+uxOlt0/6lt07M+UwUY+AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-12T21:08:09.367432Z"},"content_sha256":"1aa2a4d5594c768da1cc4ad3674a333c481e1855c0e38ddd997a9be2aeaf7437","schema_version":"1.0","event_id":"sha256:1aa2a4d5594c768da1cc4ad3674a333c481e1855c0e38ddd997a9be2aeaf7437"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/4GOKJT5RFNJLVVWL6TAGDZLKMC/bundle.json","state_url":"https://pith.science/pith/4GOKJT5RFNJLVVWL6TAGDZLKMC/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/4GOKJT5RFNJLVVWL6TAGDZLKMC/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":"2026-08-12T21:08:09Z","links":{"resolver":"https://pith.science/pith/4GOKJT5RFNJLVVWL6TAGDZLKMC","bundle":"https://pith.science/pith/4GOKJT5RFNJLVVWL6TAGDZLKMC/bundle.json","state":"https://pith.science/pith/4GOKJT5RFNJLVVWL6TAGDZLKMC/state.json","well_known_bundle":"https://pith.science/.well-known/pith/4GOKJT5RFNJLVVWL6TAGDZLKMC/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:4GOKJT5RFNJLVVWL6TAGDZLKMC","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":"3fddab77ed21fa78d62c0e3a2e4e7fc237604845e390808090701194473d7965","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2026-06-22T14:00:11Z","title_canon_sha256":"2d01f7061fe6da6cb4cb3050f891563a3c274d3cfc2315bffaf8ad403a602d51"},"schema_version":"1.0","source":{"id":"2606.23363","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.23363","created_at":"2026-06-23T03:14:17Z"},{"alias_kind":"arxiv_version","alias_value":"2606.23363v1","created_at":"2026-06-23T03:14:17Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.23363","created_at":"2026-06-23T03:14:17Z"},{"alias_kind":"pith_short_12","alias_value":"4GOKJT5RFNJL","created_at":"2026-06-23T03:14:17Z"},{"alias_kind":"pith_short_16","alias_value":"4GOKJT5RFNJLVVWL","created_at":"2026-06-23T03:14:17Z"},{"alias_kind":"pith_short_8","alias_value":"4GOKJT5R","created_at":"2026-06-23T03:14:17Z"}],"graph_snapshots":[{"event_id":"sha256:1aa2a4d5594c768da1cc4ad3674a333c481e1855c0e38ddd997a9be2aeaf7437","target":"graph","created_at":"2026-06-23T03:14:17Z","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/2606.23363/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"To address the computational challenges arising from large-scale longitudinal data, an optimal Poisson subsampling algorithm is proposed for quantile regression. The proposed method can substantially alleviate computational burden. Under some regularity conditions, we derive the asymptotic properties of the estimators from weighted quantile generalized estimating equations. For practical implementation, an efficient algorithm is proposed for parameter estimation. Furthermore, asymptotic theory is established for penalized weighted smooth quantile generalized estimating equations, and regulariz","authors_text":"Chunjing Li, Jiahui Zhang, Xiaohui Yuan","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2026-06-22T14:00:11Z","title":"Optimal Poisson subsampling for quantile regression with large-scale longitudinal data"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.23363","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:d280a8eb631f349ca9503484c3637fb4aca6e8d1bfdcfd36013921988cdcc75b","target":"record","created_at":"2026-06-23T03:14:17Z","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":"3fddab77ed21fa78d62c0e3a2e4e7fc237604845e390808090701194473d7965","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2026-06-22T14:00:11Z","title_canon_sha256":"2d01f7061fe6da6cb4cb3050f891563a3c274d3cfc2315bffaf8ad403a602d51"},"schema_version":"1.0","source":{"id":"2606.23363","kind":"arxiv","version":1}},"canonical_sha256":"e19ca4cfb12b52bad6cbf4c061e56a60aa2def86639e57735d7c9b460e89ad32","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e19ca4cfb12b52bad6cbf4c061e56a60aa2def86639e57735d7c9b460e89ad32","first_computed_at":"2026-06-23T03:14:17.863030Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-23T03:14:17.863030Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"fRCn2qFi2v+eN6WxWsbhIB1Xa+xaj0ka1hmLAe/Y4MxEw3sKECarC+0bwYN+sY/ZfsKck/hBVYi4N7S82NC8AA==","signature_status":"signed_v1","signed_at":"2026-06-23T03:14:17.863449Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.23363","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:d280a8eb631f349ca9503484c3637fb4aca6e8d1bfdcfd36013921988cdcc75b","sha256:1aa2a4d5594c768da1cc4ad3674a333c481e1855c0e38ddd997a9be2aeaf7437"],"state_sha256":"d0f41b71f48a86f061ac261f28b2b1ba2e6d447b74ec9eba69920eb15c11a6a0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ipM6820aLMTKip2Ird/seWZ4qz54kG2YlpB0XBzS+VKeAqCW3r5fKAlmt6LdT8qbsTEezQlG4T9hKuMBJ6H2Dw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-12T21:08:09.372967Z","bundle_sha256":"6c72b9dc7de4f5ab33aece3a5a1ddc71086e7160c2c98e079ef488780519ba1e"}}