{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:ZEM3FN736AXBDA35TDVDKTTLUX","short_pith_number":"pith:ZEM3FN73","schema_version":"1.0","canonical_sha256":"c919b2b7fbf02e11837d98ea354e6ba5ec5f40f8eabaac448b3d1277192f2303","source":{"kind":"arxiv","id":"1704.04806","version":1},"attestation_state":"computed","paper":{"title":"Simultaneous Inference for High Dimensional Mean Vectors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Wei Biao Wu, Zhipeng Lou","submitted_at":"2017-04-16T18:21:18Z","abstract_excerpt":"Let $X_1, \\ldots, X_n\\in\\mathbb{R}^p$ be i.i.d. random vectors. We aim to perform simultaneous inference for the mean vector $\\mathbb{E} (X_i)$ with finite polynomial moments and an ultra high dimension. Our approach is based on the truncated sample mean vector. A Gaussian approximation result is derived for the latter under the very mild finite polynomial ($(2+\\theta)$-th) moment condition and the dimension $p$ can be allowed to grow exponentially with the sample size $n$. Based on this result, we propose an innovative resampling method to construct simultaneous confidence intervals for mean "},"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":"1704.04806","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2017-04-16T18:21:18Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"c477bf76b86e40c9989c01ac3e0979531d122052db4c79f8c16d76b366253988","abstract_canon_sha256":"ed3ae77ad0ab2b463ba8e0818d2e85232ff45f0024e6ad7ef492cae00ad102fd"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:46:17.675517Z","signature_b64":"Gjxx5/tG4W4JcUpTpPrUB1p+N/vB4irGBlpcrWv6nAjTfxE6koXaQPe+X65TDb9Ta8wUfzrxDHUUqWOu0YYLDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c919b2b7fbf02e11837d98ea354e6ba5ec5f40f8eabaac448b3d1277192f2303","last_reissued_at":"2026-05-18T00:46:17.675068Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:46:17.675068Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Simultaneous Inference for High Dimensional Mean Vectors","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Wei Biao Wu, Zhipeng Lou","submitted_at":"2017-04-16T18:21:18Z","abstract_excerpt":"Let $X_1, \\ldots, X_n\\in\\mathbb{R}^p$ be i.i.d. random vectors. We aim to perform simultaneous inference for the mean vector $\\mathbb{E} (X_i)$ with finite polynomial moments and an ultra high dimension. Our approach is based on the truncated sample mean vector. A Gaussian approximation result is derived for the latter under the very mild finite polynomial ($(2+\\theta)$-th) moment condition and the dimension $p$ can be allowed to grow exponentially with the sample size $n$. Based on this result, we propose an innovative resampling method to construct simultaneous confidence intervals for mean "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1704.04806","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":"1704.04806","created_at":"2026-05-18T00:46:17.675135+00:00"},{"alias_kind":"arxiv_version","alias_value":"1704.04806v1","created_at":"2026-05-18T00:46:17.675135+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1704.04806","created_at":"2026-05-18T00:46:17.675135+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZEM3FN736AXB","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZEM3FN736AXBDA35","created_at":"2026-05-18T12:31:59.375834+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZEM3FN73","created_at":"2026-05-18T12:31:59.375834+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/ZEM3FN736AXBDA35TDVDKTTLUX","json":"https://pith.science/pith/ZEM3FN736AXBDA35TDVDKTTLUX.json","graph_json":"https://pith.science/api/pith-number/ZEM3FN736AXBDA35TDVDKTTLUX/graph.json","events_json":"https://pith.science/api/pith-number/ZEM3FN736AXBDA35TDVDKTTLUX/events.json","paper":"https://pith.science/paper/ZEM3FN73"},"agent_actions":{"view_html":"https://pith.science/pith/ZEM3FN736AXBDA35TDVDKTTLUX","download_json":"https://pith.science/pith/ZEM3FN736AXBDA35TDVDKTTLUX.json","view_paper":"https://pith.science/paper/ZEM3FN73","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1704.04806&json=true","fetch_graph":"https://pith.science/api/pith-number/ZEM3FN736AXBDA35TDVDKTTLUX/graph.json","fetch_events":"https://pith.science/api/pith-number/ZEM3FN736AXBDA35TDVDKTTLUX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZEM3FN736AXBDA35TDVDKTTLUX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZEM3FN736AXBDA35TDVDKTTLUX/action/storage_attestation","attest_author":"https://pith.science/pith/ZEM3FN736AXBDA35TDVDKTTLUX/action/author_attestation","sign_citation":"https://pith.science/pith/ZEM3FN736AXBDA35TDVDKTTLUX/action/citation_signature","submit_replication":"https://pith.science/pith/ZEM3FN736AXBDA35TDVDKTTLUX/action/replication_record"}},"created_at":"2026-05-18T00:46:17.675135+00:00","updated_at":"2026-05-18T00:46:17.675135+00:00"}