{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:K4AYWEDVRXBBABFZEBKRW527OS","short_pith_number":"pith:K4AYWEDV","canonical_record":{"source":{"id":"2504.09253","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-04-12T15:22:21Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"7ffe032136e0a9fc9a2fe871875b9136e0fe6a483b65e0943aa669eaceaec40f","abstract_canon_sha256":"a855c4d277570e34ce6e8b6d2aa523ce1945b138a88d588763d7e262b255fbe2"},"schema_version":"1.0"},"canonical_sha256":"57018b10758dc21004b920551b775f749c9d8fe2d938c48ce38efe13b783397f","source":{"kind":"arxiv","id":"2504.09253","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.09253","created_at":"2026-07-05T10:48:34Z"},{"alias_kind":"arxiv_version","alias_value":"2504.09253v1","created_at":"2026-07-05T10:48:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.09253","created_at":"2026-07-05T10:48:34Z"},{"alias_kind":"pith_short_12","alias_value":"K4AYWEDVRXBB","created_at":"2026-07-05T10:48:34Z"},{"alias_kind":"pith_short_16","alias_value":"K4AYWEDVRXBBABFZ","created_at":"2026-07-05T10:48:34Z"},{"alias_kind":"pith_short_8","alias_value":"K4AYWEDV","created_at":"2026-07-05T10:48:34Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:K4AYWEDVRXBBABFZEBKRW527OS","target":"record","payload":{"canonical_record":{"source":{"id":"2504.09253","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-04-12T15:22:21Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"7ffe032136e0a9fc9a2fe871875b9136e0fe6a483b65e0943aa669eaceaec40f","abstract_canon_sha256":"a855c4d277570e34ce6e8b6d2aa523ce1945b138a88d588763d7e262b255fbe2"},"schema_version":"1.0"},"canonical_sha256":"57018b10758dc21004b920551b775f749c9d8fe2d938c48ce38efe13b783397f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:48:34.094760Z","signature_b64":"whHZKuTMXcqIxveBkCIGFC4bNPHp+EiEMHkN4TQUX3qfYOPFA5hKLkYJG4iAO1IowoJK4iTNGCkwPmye7xhwAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"57018b10758dc21004b920551b775f749c9d8fe2d938c48ce38efe13b783397f","last_reissued_at":"2026-07-05T10:48:34.094310Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:48:34.094310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2504.09253","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":"2026-07-05T10:48:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NO60yEYIvoaie42jPGuUMvmqnVsEiUPQaNg3w9JANV7QkLU0RqC1CBqxZG31+Y5KXi6nam9/krA96n+jlvQfDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T20:17:15.013329Z"},"content_sha256":"e54ffde52064fde517c343643ace241382ac8dec24226f2631b24fa3b93b556e","schema_version":"1.0","event_id":"sha256:e54ffde52064fde517c343643ace241382ac8dec24226f2631b24fa3b93b556e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:K4AYWEDVRXBBABFZEBKRW527OS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Statistical Inference for High-Dimensional Robust Linear Regression Models via Recursive Online-Score Estimation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ME","authors_text":"Dian Zheng, Lingzhou Xue","submitted_at":"2025-04-12T15:22:21Z","abstract_excerpt":"This paper introduces a novel framework for estimation and inference in penalized M-estimators applied to robust high-dimensional linear regression models. Traditional methods for high-dimensional statistical inference, which predominantly rely on convex likelihood-based approaches, struggle to address the nonconvexity inherent in penalized M-estimation with nonconvex objective functions. Our proposed method extends the recursive online score estimation (ROSE) framework of Shi et al. (2021) to robust high-dimensional settings by developing a recursive score equation based on penalized M-estima"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.09253","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/2504.09253/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":"2026-07-05T10:48:34Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I7AvNpSw5/x/1w7HB1P/5kUQxGSaBToAsth7uv/rLus6Wxe7nu/84N+5q3Fp/6xeYgmQ47U4RqfBMZ+HnYtYBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T20:17:15.014248Z"},"content_sha256":"9f0ef8edbed1706b6ad08a7d435742badc9a10e8d553cdbbdb885db616f24f45","schema_version":"1.0","event_id":"sha256:9f0ef8edbed1706b6ad08a7d435742badc9a10e8d553cdbbdb885db616f24f45"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/K4AYWEDVRXBBABFZEBKRW527OS/bundle.json","state_url":"https://pith.science/pith/K4AYWEDVRXBBABFZEBKRW527OS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/K4AYWEDVRXBBABFZEBKRW527OS/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":"2026-08-04T20:17:15Z","links":{"resolver":"https://pith.science/pith/K4AYWEDVRXBBABFZEBKRW527OS","bundle":"https://pith.science/pith/K4AYWEDVRXBBABFZEBKRW527OS/bundle.json","state":"https://pith.science/pith/K4AYWEDVRXBBABFZEBKRW527OS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/K4AYWEDVRXBBABFZEBKRW527OS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:K4AYWEDVRXBBABFZEBKRW527OS","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":"a855c4d277570e34ce6e8b6d2aa523ce1945b138a88d588763d7e262b255fbe2","cross_cats_sorted":["math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-04-12T15:22:21Z","title_canon_sha256":"7ffe032136e0a9fc9a2fe871875b9136e0fe6a483b65e0943aa669eaceaec40f"},"schema_version":"1.0","source":{"id":"2504.09253","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.09253","created_at":"2026-07-05T10:48:34Z"},{"alias_kind":"arxiv_version","alias_value":"2504.09253v1","created_at":"2026-07-05T10:48:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.09253","created_at":"2026-07-05T10:48:34Z"},{"alias_kind":"pith_short_12","alias_value":"K4AYWEDVRXBB","created_at":"2026-07-05T10:48:34Z"},{"alias_kind":"pith_short_16","alias_value":"K4AYWEDVRXBBABFZ","created_at":"2026-07-05T10:48:34Z"},{"alias_kind":"pith_short_8","alias_value":"K4AYWEDV","created_at":"2026-07-05T10:48:34Z"}],"graph_snapshots":[{"event_id":"sha256:9f0ef8edbed1706b6ad08a7d435742badc9a10e8d553cdbbdb885db616f24f45","target":"graph","created_at":"2026-07-05T10:48:34Z","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/2504.09253/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"This paper introduces a novel framework for estimation and inference in penalized M-estimators applied to robust high-dimensional linear regression models. Traditional methods for high-dimensional statistical inference, which predominantly rely on convex likelihood-based approaches, struggle to address the nonconvexity inherent in penalized M-estimation with nonconvex objective functions. Our proposed method extends the recursive online score estimation (ROSE) framework of Shi et al. (2021) to robust high-dimensional settings by developing a recursive score equation based on penalized M-estima","authors_text":"Dian Zheng, Lingzhou Xue","cross_cats":["math.ST","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-04-12T15:22:21Z","title":"Statistical Inference for High-Dimensional Robust Linear Regression Models via Recursive Online-Score Estimation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.09253","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:e54ffde52064fde517c343643ace241382ac8dec24226f2631b24fa3b93b556e","target":"record","created_at":"2026-07-05T10:48:34Z","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":"a855c4d277570e34ce6e8b6d2aa523ce1945b138a88d588763d7e262b255fbe2","cross_cats_sorted":["math.ST","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2025-04-12T15:22:21Z","title_canon_sha256":"7ffe032136e0a9fc9a2fe871875b9136e0fe6a483b65e0943aa669eaceaec40f"},"schema_version":"1.0","source":{"id":"2504.09253","kind":"arxiv","version":1}},"canonical_sha256":"57018b10758dc21004b920551b775f749c9d8fe2d938c48ce38efe13b783397f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"57018b10758dc21004b920551b775f749c9d8fe2d938c48ce38efe13b783397f","first_computed_at":"2026-07-05T10:48:34.094310Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:48:34.094310Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"whHZKuTMXcqIxveBkCIGFC4bNPHp+EiEMHkN4TQUX3qfYOPFA5hKLkYJG4iAO1IowoJK4iTNGCkwPmye7xhwAg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:48:34.094760Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.09253","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:e54ffde52064fde517c343643ace241382ac8dec24226f2631b24fa3b93b556e","sha256:9f0ef8edbed1706b6ad08a7d435742badc9a10e8d553cdbbdb885db616f24f45"],"state_sha256":"4393f34a1cfaf2ac80f511f1e527b8dc186f2b19982d25a9e4033753b26d9999"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+k0tWzDpE/VfD7gN1Xtjd9O9lVFMDWgzg6eWZPoqKbuoV8hObIhrgmt6ZGe38362xkAXmwAKA5ZkqMMl505uDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T20:17:15.021920Z","bundle_sha256":"c6b8a46d831a8bf366246da126e5c188c0d0192f0b1128ec308b98146eea5629"}}