{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:OTCGS4W5MP6UA2TCNC6F6BO2PJ","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":"9057a916189f818ba143a820c9d885a457056645e32abc612de140cd6aad34c8","cross_cats_sorted":["stat.ME","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-03-02T14:25:46Z","title_canon_sha256":"31eaa392d4701517c498e43a360f4152935a7d0b9d2d5096a6789a1e6e6b8812"},"schema_version":"1.0","source":{"id":"1903.00708","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.00708","created_at":"2026-05-17T23:52:13Z"},{"alias_kind":"arxiv_version","alias_value":"1903.00708v1","created_at":"2026-05-17T23:52:13Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.00708","created_at":"2026-05-17T23:52:13Z"},{"alias_kind":"pith_short_12","alias_value":"OTCGS4W5MP6U","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_16","alias_value":"OTCGS4W5MP6UA2TC","created_at":"2026-05-18T12:33:24Z"},{"alias_kind":"pith_short_8","alias_value":"OTCGS4W5","created_at":"2026-05-18T12:33:24Z"}],"graph_snapshots":[{"event_id":"sha256:10ea477cb0c0adc41fd279fa22ab3ba3e8131af6e3cd558e3c82162e3276bd21","target":"graph","created_at":"2026-05-17T23:52:13Z","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"},"paper":{"abstract_excerpt":"In many statistical modeling frameworks, goodness-of-fit tests are typically administered to the estimated residuals. In the time series setting, whiteness of the residuals is assessed using the sample autocorrelation function. For many time series models, especially those used for financial time series, the key assumption on the residuals is that they are in fact independent and not just uncorrelated. In this paper, we apply the auto-distance covariance function (ADCV) to evaluate the serial dependence of the estimated residuals. Distance covariance can discriminate between dependence and ind","authors_text":"Phyllis Wan, Richard A. Davis","cross_cats":["stat.ME","stat.TH"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-03-02T14:25:46Z","title":"Goodness-of-Fit Testing for Time Series Models via Distance Covariance"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.00708","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":"sha256:7dac6d105104ce2446c7f517dafe9223b9e3898bc1cfff0eedc613e945e99513","target":"record","created_at":"2026-05-17T23:52:13Z","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":"9057a916189f818ba143a820c9d885a457056645e32abc612de140cd6aad34c8","cross_cats_sorted":["stat.ME","stat.TH"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2019-03-02T14:25:46Z","title_canon_sha256":"31eaa392d4701517c498e43a360f4152935a7d0b9d2d5096a6789a1e6e6b8812"},"schema_version":"1.0","source":{"id":"1903.00708","kind":"arxiv","version":1}},"canonical_sha256":"74c46972dd63fd406a6268bc5f05da7a5a79fc319f2d6acf89765e3c8e44585a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"74c46972dd63fd406a6268bc5f05da7a5a79fc319f2d6acf89765e3c8e44585a","first_computed_at":"2026-05-17T23:52:13.558176Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:52:13.558176Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"7q3CeZRhVaLZPLktIvnfC8tcaucGqBkf4SJY9agCKFGpmNkWebsd5NEF3tXPvS6rNtM5dEWhpKaOIUH4qKfZBQ==","signature_status":"signed_v1","signed_at":"2026-05-17T23:52:13.558833Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.00708","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7dac6d105104ce2446c7f517dafe9223b9e3898bc1cfff0eedc613e945e99513","sha256:10ea477cb0c0adc41fd279fa22ab3ba3e8131af6e3cd558e3c82162e3276bd21"],"state_sha256":"a448da3a983d35a71f94355aba10d602422b459eafc1bce6d677b0b2559c0a40"}