{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:YQLNL5WSCH2P5QKJUVF46BJCWT","short_pith_number":"pith:YQLNL5WS","schema_version":"1.0","canonical_sha256":"c416d5f6d211f4fec149a54bcf0522b4c85f159723fc61350fad6fa79f12195a","source":{"kind":"arxiv","id":"1808.07704","version":1},"attestation_state":"computed","paper":{"title":"Data-adaptive trimming of the Hill estimator and detection of outliers in the extremes of heavy-tailed data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Michael Kallitsis, Shrijita Bhattacharya, Stilian Stoev","submitted_at":"2018-08-23T11:36:27Z","abstract_excerpt":"We introduce a trimmed version of the Hill estimator for the index of a heavy-tailed distribution, which is robust to perturbations in the extreme order statistics. In the ideal Pareto setting, the estimator is essentially finite-sample efficient among all unbiased estimators with a given strict upper break-down point. For general heavy-tailed models, we establish the asymptotic normality of the estimator under second order regular variation conditions and also show it is minimax rate-optimal in the Hall class of distributions. We also develop an automatic, data-driven method for the choice of"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"1808.07704","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-08-23T11:36:27Z","cross_cats_sorted":[],"title_canon_sha256":"483641bd852239f6afc9510c33c658bf5a35f5a358c9d9349dca30fa8d96e2aa","abstract_canon_sha256":"b32ce8a1188c0572acd90b19d1ae940e32d0edd938001d3a7c3af27b567d05ba"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:07:24.464932Z","signature_b64":"0GVbummxOT3tma3xh6TFaF6KnIIIW7kgNHpEKsHK01dDGFHSmgOPl9E8dhWi9ZJkZLoaVzyemIIrpVGhCTvkAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c416d5f6d211f4fec149a54bcf0522b4c85f159723fc61350fad6fa79f12195a","last_reissued_at":"2026-05-18T00:07:24.464222Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:07:24.464222Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Data-adaptive trimming of the Hill estimator and detection of outliers in the extremes of heavy-tailed data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Michael Kallitsis, Shrijita Bhattacharya, Stilian Stoev","submitted_at":"2018-08-23T11:36:27Z","abstract_excerpt":"We introduce a trimmed version of the Hill estimator for the index of a heavy-tailed distribution, which is robust to perturbations in the extreme order statistics. In the ideal Pareto setting, the estimator is essentially finite-sample efficient among all unbiased estimators with a given strict upper break-down point. For general heavy-tailed models, we establish the asymptotic normality of the estimator under second order regular variation conditions and also show it is minimax rate-optimal in the Hall class of distributions. We also develop an automatic, data-driven method for the choice of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1808.07704","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"},"aliases":[{"alias_kind":"arxiv","alias_value":"1808.07704","created_at":"2026-05-18T00:07:24.464325+00:00"},{"alias_kind":"arxiv_version","alias_value":"1808.07704v1","created_at":"2026-05-18T00:07:24.464325+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1808.07704","created_at":"2026-05-18T00:07:24.464325+00:00"},{"alias_kind":"pith_short_12","alias_value":"YQLNL5WSCH2P","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_16","alias_value":"YQLNL5WSCH2P5QKJ","created_at":"2026-05-18T12:33:04.347982+00:00"},{"alias_kind":"pith_short_8","alias_value":"YQLNL5WS","created_at":"2026-05-18T12:33:04.347982+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YQLNL5WSCH2P5QKJUVF46BJCWT","json":"https://pith.science/pith/YQLNL5WSCH2P5QKJUVF46BJCWT.json","graph_json":"https://pith.science/api/pith-number/YQLNL5WSCH2P5QKJUVF46BJCWT/graph.json","events_json":"https://pith.science/api/pith-number/YQLNL5WSCH2P5QKJUVF46BJCWT/events.json","paper":"https://pith.science/paper/YQLNL5WS"},"agent_actions":{"view_html":"https://pith.science/pith/YQLNL5WSCH2P5QKJUVF46BJCWT","download_json":"https://pith.science/pith/YQLNL5WSCH2P5QKJUVF46BJCWT.json","view_paper":"https://pith.science/paper/YQLNL5WS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1808.07704&json=true","fetch_graph":"https://pith.science/api/pith-number/YQLNL5WSCH2P5QKJUVF46BJCWT/graph.json","fetch_events":"https://pith.science/api/pith-number/YQLNL5WSCH2P5QKJUVF46BJCWT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YQLNL5WSCH2P5QKJUVF46BJCWT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YQLNL5WSCH2P5QKJUVF46BJCWT/action/storage_attestation","attest_author":"https://pith.science/pith/YQLNL5WSCH2P5QKJUVF46BJCWT/action/author_attestation","sign_citation":"https://pith.science/pith/YQLNL5WSCH2P5QKJUVF46BJCWT/action/citation_signature","submit_replication":"https://pith.science/pith/YQLNL5WSCH2P5QKJUVF46BJCWT/action/replication_record"}},"created_at":"2026-05-18T00:07:24.464325+00:00","updated_at":"2026-05-18T00:07:24.464325+00:00"}