{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:WRC7LM2LAXIW3VZ6LXQYDUW5Z4","short_pith_number":"pith:WRC7LM2L","canonical_record":{"source":{"id":"1708.05439","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-08-17T20:51:01Z","cross_cats_sorted":[],"title_canon_sha256":"4c2525dd6247541fd7fc28fcaaa37be5678505579280d136701436428f802f5b","abstract_canon_sha256":"6bfd40fc134a397268e5358be75d5d2821c74d94e099ec3574fa27cef23a7f33"},"schema_version":"1.0"},"canonical_sha256":"b445f5b34b05d16dd73e5de181d2ddcf31497e546b500067453576239d763168","source":{"kind":"arxiv","id":"1708.05439","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.05439","created_at":"2026-05-18T00:37:41Z"},{"alias_kind":"arxiv_version","alias_value":"1708.05439v2","created_at":"2026-05-18T00:37:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.05439","created_at":"2026-05-18T00:37:41Z"},{"alias_kind":"pith_short_12","alias_value":"WRC7LM2LAXIW","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_16","alias_value":"WRC7LM2LAXIW3VZ6","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_8","alias_value":"WRC7LM2L","created_at":"2026-05-18T12:31:53Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:WRC7LM2LAXIW3VZ6LXQYDUW5Z4","target":"record","payload":{"canonical_record":{"source":{"id":"1708.05439","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-08-17T20:51:01Z","cross_cats_sorted":[],"title_canon_sha256":"4c2525dd6247541fd7fc28fcaaa37be5678505579280d136701436428f802f5b","abstract_canon_sha256":"6bfd40fc134a397268e5358be75d5d2821c74d94e099ec3574fa27cef23a7f33"},"schema_version":"1.0"},"canonical_sha256":"b445f5b34b05d16dd73e5de181d2ddcf31497e546b500067453576239d763168","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:37:41.826901Z","signature_b64":"I1BNHTtU51J2NFwf+1V0b708ePeZFfpLV8FvH8SjQ9veBb3DFWJ/5lBJmMx/eDJcbpf0lWnAq+dHGNopc/JRDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b445f5b34b05d16dd73e5de181d2ddcf31497e546b500067453576239d763168","last_reissued_at":"2026-05-18T00:37:41.826309Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:37:41.826309Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1708.05439","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":"2026-05-18T00:37:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iCxiHShz8MFI1AEpw/Gxz1eregvbehzCRp9dlM3iXw/eHDcVs6dPGJFqDpbhdLDcDqd+XHWQfGxd//+9ILOyAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T07:40:23.303061Z"},"content_sha256":"19f95501d6962041abff53685f7d2ba02395cbf30cf83e5fa987fe2ac4c70854","schema_version":"1.0","event_id":"sha256:19f95501d6962041abff53685f7d2ba02395cbf30cf83e5fa987fe2ac4c70854"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:WRC7LM2LAXIW3VZ6LXQYDUW5Z4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Penalized Maximum Tangent Likelihood Estimation and Robust Variable Selection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Shaobo Li, Yang Li, Yan Yu, Yichen Qin","submitted_at":"2017-08-17T20:51:01Z","abstract_excerpt":"We introduce a new class of mean regression estimators -- penalized maximum tangent likelihood estimation -- for high-dimensional regression estimation and variable selection. We first explain the motivations for the key ingredient, maximum tangent likelihood estimation (MTE), and establish its asymptotic properties. We further propose a penalized MTE for variable selection and show that it is $\\sqrt{n}$-consistent, enjoys the oracle property. The proposed class of estimators consists penalized $\\ell_2$ distance, penalized exponential squared loss, penalized least trimmed square and penalized "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.05439","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":"2026-05-18T00:37:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Jtn8qI1r7KQk6gUMpugJyYRb2ebHzIv0BuvWAJiX43GBo2RiDwH1yJ8BhkvazcfN0BC695lWW30bF8RDwuBiBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T07:40:23.303411Z"},"content_sha256":"3665e1416ab41d70d95f78f41699bbe3defa750858c91201b6069ab5a631645e","schema_version":"1.0","event_id":"sha256:3665e1416ab41d70d95f78f41699bbe3defa750858c91201b6069ab5a631645e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WRC7LM2LAXIW3VZ6LXQYDUW5Z4/bundle.json","state_url":"https://pith.science/pith/WRC7LM2LAXIW3VZ6LXQYDUW5Z4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WRC7LM2LAXIW3VZ6LXQYDUW5Z4/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":"2026-06-02T07:40:23Z","links":{"resolver":"https://pith.science/pith/WRC7LM2LAXIW3VZ6LXQYDUW5Z4","bundle":"https://pith.science/pith/WRC7LM2LAXIW3VZ6LXQYDUW5Z4/bundle.json","state":"https://pith.science/pith/WRC7LM2LAXIW3VZ6LXQYDUW5Z4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WRC7LM2LAXIW3VZ6LXQYDUW5Z4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:WRC7LM2LAXIW3VZ6LXQYDUW5Z4","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":"6bfd40fc134a397268e5358be75d5d2821c74d94e099ec3574fa27cef23a7f33","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-08-17T20:51:01Z","title_canon_sha256":"4c2525dd6247541fd7fc28fcaaa37be5678505579280d136701436428f802f5b"},"schema_version":"1.0","source":{"id":"1708.05439","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1708.05439","created_at":"2026-05-18T00:37:41Z"},{"alias_kind":"arxiv_version","alias_value":"1708.05439v2","created_at":"2026-05-18T00:37:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1708.05439","created_at":"2026-05-18T00:37:41Z"},{"alias_kind":"pith_short_12","alias_value":"WRC7LM2LAXIW","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_16","alias_value":"WRC7LM2LAXIW3VZ6","created_at":"2026-05-18T12:31:53Z"},{"alias_kind":"pith_short_8","alias_value":"WRC7LM2L","created_at":"2026-05-18T12:31:53Z"}],"graph_snapshots":[{"event_id":"sha256:3665e1416ab41d70d95f78f41699bbe3defa750858c91201b6069ab5a631645e","target":"graph","created_at":"2026-05-18T00:37:41Z","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":"We introduce a new class of mean regression estimators -- penalized maximum tangent likelihood estimation -- for high-dimensional regression estimation and variable selection. We first explain the motivations for the key ingredient, maximum tangent likelihood estimation (MTE), and establish its asymptotic properties. We further propose a penalized MTE for variable selection and show that it is $\\sqrt{n}$-consistent, enjoys the oracle property. The proposed class of estimators consists penalized $\\ell_2$ distance, penalized exponential squared loss, penalized least trimmed square and penalized ","authors_text":"Shaobo Li, Yang Li, Yan Yu, Yichen Qin","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-08-17T20:51:01Z","title":"Penalized Maximum Tangent Likelihood Estimation and Robust Variable Selection"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1708.05439","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":"sha256:19f95501d6962041abff53685f7d2ba02395cbf30cf83e5fa987fe2ac4c70854","target":"record","created_at":"2026-05-18T00:37:41Z","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":"6bfd40fc134a397268e5358be75d5d2821c74d94e099ec3574fa27cef23a7f33","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2017-08-17T20:51:01Z","title_canon_sha256":"4c2525dd6247541fd7fc28fcaaa37be5678505579280d136701436428f802f5b"},"schema_version":"1.0","source":{"id":"1708.05439","kind":"arxiv","version":2}},"canonical_sha256":"b445f5b34b05d16dd73e5de181d2ddcf31497e546b500067453576239d763168","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b445f5b34b05d16dd73e5de181d2ddcf31497e546b500067453576239d763168","first_computed_at":"2026-05-18T00:37:41.826309Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:37:41.826309Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"I1BNHTtU51J2NFwf+1V0b708ePeZFfpLV8FvH8SjQ9veBb3DFWJ/5lBJmMx/eDJcbpf0lWnAq+dHGNopc/JRDw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:37:41.826901Z","signed_message":"canonical_sha256_bytes"},"source_id":"1708.05439","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:19f95501d6962041abff53685f7d2ba02395cbf30cf83e5fa987fe2ac4c70854","sha256:3665e1416ab41d70d95f78f41699bbe3defa750858c91201b6069ab5a631645e"],"state_sha256":"f729fa384bcbb9e30f6c75a220ad38d4d9010d871c668222af57b91ca7364bf9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"PNOZbr8JOEmiQEviJj6M00UNVpWAYLC3VNzgvljb7sN1yUy4LfEiaUj6JI6ULStK2fMeQdJBOKkSqUIRPCzIAQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T07:40:23.305416Z","bundle_sha256":"7f1be906ca738e0e4a728ab6d641aa370de0073c632556077376f80da1bad9b9"}}