{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:TBEXXL4EN3EJECJRSKN4V4VHJN","short_pith_number":"pith:TBEXXL4E","schema_version":"1.0","canonical_sha256":"98497baf846ec8920931929bcaf2a74b4ca7a7756991e6e875ff5f0bf66e6c10","source":{"kind":"arxiv","id":"1309.2435","version":1},"attestation_state":"computed","paper":{"title":"Bayesian Wavelet Shrinkage of the Haar-Fisz Transformed Wavelet Periodogram","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Guy P. Nason, Kara N. Stevens","submitted_at":"2013-09-10T10:02:22Z","abstract_excerpt":"It is increasingly being realised that many real world time series are not stationary and exhibit evolving second-order autocovariance or spectral structure. This article introduces a Bayesian approach for modelling the evolving wavelet spectrum of a locally stationary wavelet time series. Our new method works by combining the advantages of a Haar-Fisz transformed spectrum with a simple, but powerful, Bayesian wavelet shrinkage method. Our new method produces excellent and stable spectral estimates and this is demonstrated via simulated data and on differenced infant ECG data. A major addition"},"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":"1309.2435","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2013-09-10T10:02:22Z","cross_cats_sorted":[],"title_canon_sha256":"e82a5f843e0469bceeaf7ba779712167a3446d7e7c2c75863972d336d96a0aa2","abstract_canon_sha256":"36b0c60712b8a84dcb4c97657a6cfa6939dfb57378fccdd748a75b804a9d405f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:13:44.666594Z","signature_b64":"OvcUBiJynnkHttvNCHwRI5Ef7KuqBoRCh53vZqmMtNyc+0xIWbu5NddIaJkdzbjK5bFW1btrJ11fSBvicBIeDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"98497baf846ec8920931929bcaf2a74b4ca7a7756991e6e875ff5f0bf66e6c10","last_reissued_at":"2026-05-18T03:13:44.665729Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:13:44.665729Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian Wavelet Shrinkage of the Haar-Fisz Transformed Wavelet Periodogram","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Guy P. Nason, Kara N. Stevens","submitted_at":"2013-09-10T10:02:22Z","abstract_excerpt":"It is increasingly being realised that many real world time series are not stationary and exhibit evolving second-order autocovariance or spectral structure. This article introduces a Bayesian approach for modelling the evolving wavelet spectrum of a locally stationary wavelet time series. Our new method works by combining the advantages of a Haar-Fisz transformed spectrum with a simple, but powerful, Bayesian wavelet shrinkage method. Our new method produces excellent and stable spectral estimates and this is demonstrated via simulated data and on differenced infant ECG data. A major addition"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1309.2435","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":"1309.2435","created_at":"2026-05-18T03:13:44.665908+00:00"},{"alias_kind":"arxiv_version","alias_value":"1309.2435v1","created_at":"2026-05-18T03:13:44.665908+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1309.2435","created_at":"2026-05-18T03:13:44.665908+00:00"},{"alias_kind":"pith_short_12","alias_value":"TBEXXL4EN3EJ","created_at":"2026-05-18T12:27:59.945178+00:00"},{"alias_kind":"pith_short_16","alias_value":"TBEXXL4EN3EJECJR","created_at":"2026-05-18T12:27:59.945178+00:00"},{"alias_kind":"pith_short_8","alias_value":"TBEXXL4E","created_at":"2026-05-18T12:27:59.945178+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/TBEXXL4EN3EJECJRSKN4V4VHJN","json":"https://pith.science/pith/TBEXXL4EN3EJECJRSKN4V4VHJN.json","graph_json":"https://pith.science/api/pith-number/TBEXXL4EN3EJECJRSKN4V4VHJN/graph.json","events_json":"https://pith.science/api/pith-number/TBEXXL4EN3EJECJRSKN4V4VHJN/events.json","paper":"https://pith.science/paper/TBEXXL4E"},"agent_actions":{"view_html":"https://pith.science/pith/TBEXXL4EN3EJECJRSKN4V4VHJN","download_json":"https://pith.science/pith/TBEXXL4EN3EJECJRSKN4V4VHJN.json","view_paper":"https://pith.science/paper/TBEXXL4E","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1309.2435&json=true","fetch_graph":"https://pith.science/api/pith-number/TBEXXL4EN3EJECJRSKN4V4VHJN/graph.json","fetch_events":"https://pith.science/api/pith-number/TBEXXL4EN3EJECJRSKN4V4VHJN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/TBEXXL4EN3EJECJRSKN4V4VHJN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/TBEXXL4EN3EJECJRSKN4V4VHJN/action/storage_attestation","attest_author":"https://pith.science/pith/TBEXXL4EN3EJECJRSKN4V4VHJN/action/author_attestation","sign_citation":"https://pith.science/pith/TBEXXL4EN3EJECJRSKN4V4VHJN/action/citation_signature","submit_replication":"https://pith.science/pith/TBEXXL4EN3EJECJRSKN4V4VHJN/action/replication_record"}},"created_at":"2026-05-18T03:13:44.665908+00:00","updated_at":"2026-05-18T03:13:44.665908+00:00"}