{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:RWKSDC7S7DA4REYSWU4PYYSPTE","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":"453c171eb368dbee3dece0301f4114a8e8d4c25921e0d99126777b6adfe58f75","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2026-05-15T04:07:08Z","title_canon_sha256":"42b1f4734454f75060955fd0fbf425eb63113ad9a63a47812f35fddd093afbcd"},"schema_version":"1.0","source":{"id":"2605.15596","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.15596","created_at":"2026-05-20T00:01:07Z"},{"alias_kind":"arxiv_version","alias_value":"2605.15596v1","created_at":"2026-05-20T00:01:07Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.15596","created_at":"2026-05-20T00:01:07Z"},{"alias_kind":"pith_short_12","alias_value":"RWKSDC7S7DA4","created_at":"2026-05-20T00:01:07Z"},{"alias_kind":"pith_short_16","alias_value":"RWKSDC7S7DA4REYS","created_at":"2026-05-20T00:01:07Z"},{"alias_kind":"pith_short_8","alias_value":"RWKSDC7S","created_at":"2026-05-20T00:01:07Z"}],"graph_snapshots":[{"event_id":"sha256:5edd99164024d3623919f450cd67c321a41f30cb028c687b78d1e9d0686a2720","target":"graph","created_at":"2026-05-20T00:01:07Z","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"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T19:34:34.901565Z","status":"skipped","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-19T17:41:56.058416Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.15596/integrity.json","findings":[],"snapshot_sha256":"f36a4cce65107bdbec4932013977742b2632fe5ce0b182984a3303dcd735fcf4","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Prewhitening is a common approach to deal with strong autocorrelation. In this article, we propose a new approach called tail postcoloring, motivated by it. It uses parametric models to project, or color back, the neglected tail autocovariances in nonparametric estimators onto the final estimator. This approach bridges the non-parametric variance estimator and the parametric coloring model through a scaling factor. It automatically switches between these two arms using a bandwidth parameter, without the need to transform the entire dataset into residuals, as in the standard prewhitening approa","authors_text":"Kin Wai Chan, Xu Liu","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2026-05-15T04:07:08Z","title":"Tail postcoloring in long-run variance estimation of time series"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.15596","kind":"arxiv","version":1},"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":"sha256:4f8982660bcbf795d5b68411c1249390ccb6e4a298c1a64ba4a0952de17be95d","target":"record","created_at":"2026-05-20T00:01:07Z","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":"453c171eb368dbee3dece0301f4114a8e8d4c25921e0d99126777b6adfe58f75","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2026-05-15T04:07:08Z","title_canon_sha256":"42b1f4734454f75060955fd0fbf425eb63113ad9a63a47812f35fddd093afbcd"},"schema_version":"1.0","source":{"id":"2605.15596","kind":"arxiv","version":1}},"canonical_sha256":"8d95218bf2f8c1c89312b538fc624f99354092a19493e0e2434f3e02ff889893","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8d95218bf2f8c1c89312b538fc624f99354092a19493e0e2434f3e02ff889893","first_computed_at":"2026-05-20T00:01:07.218808Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-20T00:01:07.218808Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"/HhRPyu1XVPt8bqku0aTXy97/tlQb7SgxTd94Al8fhFnaJF7cLzJM/ROTbvqwT+mz/ZMBdFcfgvtVMpJU7LuBg==","signature_status":"signed_v1","signed_at":"2026-05-20T00:01:07.219579Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.15596","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4f8982660bcbf795d5b68411c1249390ccb6e4a298c1a64ba4a0952de17be95d","sha256:5edd99164024d3623919f450cd67c321a41f30cb028c687b78d1e9d0686a2720"],"state_sha256":"2f4e640c57f113c82317242d2e72f88b832b5f995453053073a4385ee17bff43"}