{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:BGNN6CKM3EUAW6KTUBKAYRELBX","short_pith_number":"pith:BGNN6CKM","schema_version":"1.0","canonical_sha256":"099adf094cd9280b7953a0540c448b0dc3afce910625baaf26415a1ef41af0c7","source":{"kind":"arxiv","id":"1806.06523","version":1},"attestation_state":"computed","paper":{"title":"A Frequency Domain Bootstrap for General Stationary Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Efstathios Paparoditis, Jens-Peter Kreiss, Marco Meyer","submitted_at":"2018-06-18T07:24:59Z","abstract_excerpt":"Existing frequency domain methods for bootstrapping time series have a limited range. Consider for instance the class of spectral mean statistics (also called integrated periodograms) which includes many important statistics in time series analysis, such as sample autocovariances and autocorrelations among other things. Essentially, such frequency domain bootstrap procedures cover the case of linear time series with independent innovations, and some even require the time series to be Gaussian. In this paper we propose a new, frequency domain bootstrap method which is consistent for a much wide"},"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":"1806.06523","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-06-18T07:24:59Z","cross_cats_sorted":[],"title_canon_sha256":"0b1df922e73fd8b716f9517f09f73d2bfd9a45af2c3236a244a29f343aa4ff3d","abstract_canon_sha256":"03915ccb7107b8640977a435a79d3ed79b7076afb148a6d3a3b3c76ab85bd85c"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:13:02.085228Z","signature_b64":"tr1fmxugD5zt/Hoj4/xEETR5h1fkbtNI1UxQHrCcx755E6znFWDIonLh4Cm1FhU7lkuy98AN4ai0UbCgUzj9DA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"099adf094cd9280b7953a0540c448b0dc3afce910625baaf26415a1ef41af0c7","last_reissued_at":"2026-05-18T00:13:02.084558Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:13:02.084558Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Frequency Domain Bootstrap for General Stationary Processes","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Efstathios Paparoditis, Jens-Peter Kreiss, Marco Meyer","submitted_at":"2018-06-18T07:24:59Z","abstract_excerpt":"Existing frequency domain methods for bootstrapping time series have a limited range. Consider for instance the class of spectral mean statistics (also called integrated periodograms) which includes many important statistics in time series analysis, such as sample autocovariances and autocorrelations among other things. Essentially, such frequency domain bootstrap procedures cover the case of linear time series with independent innovations, and some even require the time series to be Gaussian. In this paper we propose a new, frequency domain bootstrap method which is consistent for a much wide"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.06523","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":"1806.06523","created_at":"2026-05-18T00:13:02.084677+00:00"},{"alias_kind":"arxiv_version","alias_value":"1806.06523v1","created_at":"2026-05-18T00:13:02.084677+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.06523","created_at":"2026-05-18T00:13:02.084677+00:00"},{"alias_kind":"pith_short_12","alias_value":"BGNN6CKM3EUA","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_16","alias_value":"BGNN6CKM3EUAW6KT","created_at":"2026-05-18T12:32:16.446611+00:00"},{"alias_kind":"pith_short_8","alias_value":"BGNN6CKM","created_at":"2026-05-18T12:32:16.446611+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/BGNN6CKM3EUAW6KTUBKAYRELBX","json":"https://pith.science/pith/BGNN6CKM3EUAW6KTUBKAYRELBX.json","graph_json":"https://pith.science/api/pith-number/BGNN6CKM3EUAW6KTUBKAYRELBX/graph.json","events_json":"https://pith.science/api/pith-number/BGNN6CKM3EUAW6KTUBKAYRELBX/events.json","paper":"https://pith.science/paper/BGNN6CKM"},"agent_actions":{"view_html":"https://pith.science/pith/BGNN6CKM3EUAW6KTUBKAYRELBX","download_json":"https://pith.science/pith/BGNN6CKM3EUAW6KTUBKAYRELBX.json","view_paper":"https://pith.science/paper/BGNN6CKM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1806.06523&json=true","fetch_graph":"https://pith.science/api/pith-number/BGNN6CKM3EUAW6KTUBKAYRELBX/graph.json","fetch_events":"https://pith.science/api/pith-number/BGNN6CKM3EUAW6KTUBKAYRELBX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/BGNN6CKM3EUAW6KTUBKAYRELBX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/BGNN6CKM3EUAW6KTUBKAYRELBX/action/storage_attestation","attest_author":"https://pith.science/pith/BGNN6CKM3EUAW6KTUBKAYRELBX/action/author_attestation","sign_citation":"https://pith.science/pith/BGNN6CKM3EUAW6KTUBKAYRELBX/action/citation_signature","submit_replication":"https://pith.science/pith/BGNN6CKM3EUAW6KTUBKAYRELBX/action/replication_record"}},"created_at":"2026-05-18T00:13:02.084677+00:00","updated_at":"2026-05-18T00:13:02.084677+00:00"}