{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:MWI2EY3P3ZZX47GRVFXNQZSN45","short_pith_number":"pith:MWI2EY3P","schema_version":"1.0","canonical_sha256":"6591a2636fde737e7cd1a96ed8664de77a270d4cbe29d2804f915e068da9f3ca","source":{"kind":"arxiv","id":"1211.1204","version":1},"attestation_state":"computed","paper":{"title":"A note on nonparametric testing for Gaussian innovations in AR-ARCH models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Leonie Selk, Natalie Neumeyer","submitted_at":"2012-11-06T12:45:45Z","abstract_excerpt":"In this paper we consider autoregressive models with conditional autoregressive variance, including the case of homoscedastic AR-models and the case of ARCH models. Our aim is to test the hypothesis of normality for the innovations in a completely nonparametric way, i. e. without imposing parametric assumptions on the conditional mean and volatility functions. To this end the Cram\\'er-von Mises test based on the empirical distribution function of nonparametrically estimated residuals is shown to be asymptotically distribution-free. We demonstrate its good performance for finite sample sizes in"},"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":"1211.1204","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-11-06T12:45:45Z","cross_cats_sorted":[],"title_canon_sha256":"2538911969cbae117e0223c1e427c652a7f2dd452bcaac99d3232a4e42050e75","abstract_canon_sha256":"15daa3532c759e8b269c430068cb2786c821a2f3f24f212f8f7d274d44b55bc4"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:41:22.060733Z","signature_b64":"GaMCNZQfL+H7gT7n3zWs6P8MMGs0DZO/hi5YRDEBs0LVOpbj4qS5tKfS41KaRuWLCVsrYkt1EZvJiKqI6FyRBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6591a2636fde737e7cd1a96ed8664de77a270d4cbe29d2804f915e068da9f3ca","last_reissued_at":"2026-05-18T03:41:22.060088Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:41:22.060088Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A note on nonparametric testing for Gaussian innovations in AR-ARCH models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Leonie Selk, Natalie Neumeyer","submitted_at":"2012-11-06T12:45:45Z","abstract_excerpt":"In this paper we consider autoregressive models with conditional autoregressive variance, including the case of homoscedastic AR-models and the case of ARCH models. Our aim is to test the hypothesis of normality for the innovations in a completely nonparametric way, i. e. without imposing parametric assumptions on the conditional mean and volatility functions. To this end the Cram\\'er-von Mises test based on the empirical distribution function of nonparametrically estimated residuals is shown to be asymptotically distribution-free. We demonstrate its good performance for finite sample sizes in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1211.1204","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":"1211.1204","created_at":"2026-05-18T03:41:22.060163+00:00"},{"alias_kind":"arxiv_version","alias_value":"1211.1204v1","created_at":"2026-05-18T03:41:22.060163+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1211.1204","created_at":"2026-05-18T03:41:22.060163+00:00"},{"alias_kind":"pith_short_12","alias_value":"MWI2EY3P3ZZX","created_at":"2026-05-18T12:27:14.488303+00:00"},{"alias_kind":"pith_short_16","alias_value":"MWI2EY3P3ZZX47GR","created_at":"2026-05-18T12:27:14.488303+00:00"},{"alias_kind":"pith_short_8","alias_value":"MWI2EY3P","created_at":"2026-05-18T12:27:14.488303+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/MWI2EY3P3ZZX47GRVFXNQZSN45","json":"https://pith.science/pith/MWI2EY3P3ZZX47GRVFXNQZSN45.json","graph_json":"https://pith.science/api/pith-number/MWI2EY3P3ZZX47GRVFXNQZSN45/graph.json","events_json":"https://pith.science/api/pith-number/MWI2EY3P3ZZX47GRVFXNQZSN45/events.json","paper":"https://pith.science/paper/MWI2EY3P"},"agent_actions":{"view_html":"https://pith.science/pith/MWI2EY3P3ZZX47GRVFXNQZSN45","download_json":"https://pith.science/pith/MWI2EY3P3ZZX47GRVFXNQZSN45.json","view_paper":"https://pith.science/paper/MWI2EY3P","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1211.1204&json=true","fetch_graph":"https://pith.science/api/pith-number/MWI2EY3P3ZZX47GRVFXNQZSN45/graph.json","fetch_events":"https://pith.science/api/pith-number/MWI2EY3P3ZZX47GRVFXNQZSN45/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MWI2EY3P3ZZX47GRVFXNQZSN45/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MWI2EY3P3ZZX47GRVFXNQZSN45/action/storage_attestation","attest_author":"https://pith.science/pith/MWI2EY3P3ZZX47GRVFXNQZSN45/action/author_attestation","sign_citation":"https://pith.science/pith/MWI2EY3P3ZZX47GRVFXNQZSN45/action/citation_signature","submit_replication":"https://pith.science/pith/MWI2EY3P3ZZX47GRVFXNQZSN45/action/replication_record"}},"created_at":"2026-05-18T03:41:22.060163+00:00","updated_at":"2026-05-18T03:41:22.060163+00:00"}