{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:4E3JQNJZQYBOHLFMNOIUHHPHUL","short_pith_number":"pith:4E3JQNJZ","schema_version":"1.0","canonical_sha256":"e1369835398602e3acac6b91439de7a2dfb644d63489fb8ab2721a9ea16effac","source":{"kind":"arxiv","id":"1803.00293","version":1},"attestation_state":"computed","paper":{"title":"Nonparametric Analysis of Clustered Multivariate Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Denis Larocque, Hannu Oja, Ilkka P\\\"orsti, Jaakko Nevalainen","submitted_at":"2018-03-01T10:40:55Z","abstract_excerpt":"There has been a wide interest to extend univariate and multivariate nonparametric procedures to clustered and hierarchical data. Traditionally, parametric mixed models have been used to account for the correlation structures among the dependent observational units. In this work we extend multivariate nonparametric procedures for one-sample and several samples location problems to clustered data settings. The results are given for a general score function, but with an emphasis on spatial sign and rank methods. Mixed models notation involving design matrices for fixed and random effects is used"},"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":"1803.00293","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2018-03-01T10:40:55Z","cross_cats_sorted":["stat.TH"],"title_canon_sha256":"4962704962c410fc688597215893aafb833df1c9f5c08b5aa209d5501a433298","abstract_canon_sha256":"c2ec85ad8d5f38ecaa78aabbcafe7d635a77cf887c5d56ac3665d52423b6523a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:22:12.709800Z","signature_b64":"eIG00FDqXkiPqwwZsWjlb/VdN1D7aM2c9Ome6Pnq4gLO7JKXQjEaOjq2x7U9R0zkaogY5j53IrNY0y9RG1ZUBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"e1369835398602e3acac6b91439de7a2dfb644d63489fb8ab2721a9ea16effac","last_reissued_at":"2026-05-18T00:22:12.709252Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:22:12.709252Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Nonparametric Analysis of Clustered Multivariate Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.TH"],"primary_cat":"math.ST","authors_text":"Denis Larocque, Hannu Oja, Ilkka P\\\"orsti, Jaakko Nevalainen","submitted_at":"2018-03-01T10:40:55Z","abstract_excerpt":"There has been a wide interest to extend univariate and multivariate nonparametric procedures to clustered and hierarchical data. Traditionally, parametric mixed models have been used to account for the correlation structures among the dependent observational units. In this work we extend multivariate nonparametric procedures for one-sample and several samples location problems to clustered data settings. The results are given for a general score function, but with an emphasis on spatial sign and rank methods. Mixed models notation involving design matrices for fixed and random effects is used"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1803.00293","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":"1803.00293","created_at":"2026-05-18T00:22:12.709321+00:00"},{"alias_kind":"arxiv_version","alias_value":"1803.00293v1","created_at":"2026-05-18T00:22:12.709321+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1803.00293","created_at":"2026-05-18T00:22:12.709321+00:00"},{"alias_kind":"pith_short_12","alias_value":"4E3JQNJZQYBO","created_at":"2026-05-18T12:32:05.422762+00:00"},{"alias_kind":"pith_short_16","alias_value":"4E3JQNJZQYBOHLFM","created_at":"2026-05-18T12:32:05.422762+00:00"},{"alias_kind":"pith_short_8","alias_value":"4E3JQNJZ","created_at":"2026-05-18T12:32:05.422762+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/4E3JQNJZQYBOHLFMNOIUHHPHUL","json":"https://pith.science/pith/4E3JQNJZQYBOHLFMNOIUHHPHUL.json","graph_json":"https://pith.science/api/pith-number/4E3JQNJZQYBOHLFMNOIUHHPHUL/graph.json","events_json":"https://pith.science/api/pith-number/4E3JQNJZQYBOHLFMNOIUHHPHUL/events.json","paper":"https://pith.science/paper/4E3JQNJZ"},"agent_actions":{"view_html":"https://pith.science/pith/4E3JQNJZQYBOHLFMNOIUHHPHUL","download_json":"https://pith.science/pith/4E3JQNJZQYBOHLFMNOIUHHPHUL.json","view_paper":"https://pith.science/paper/4E3JQNJZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1803.00293&json=true","fetch_graph":"https://pith.science/api/pith-number/4E3JQNJZQYBOHLFMNOIUHHPHUL/graph.json","fetch_events":"https://pith.science/api/pith-number/4E3JQNJZQYBOHLFMNOIUHHPHUL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/4E3JQNJZQYBOHLFMNOIUHHPHUL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/4E3JQNJZQYBOHLFMNOIUHHPHUL/action/storage_attestation","attest_author":"https://pith.science/pith/4E3JQNJZQYBOHLFMNOIUHHPHUL/action/author_attestation","sign_citation":"https://pith.science/pith/4E3JQNJZQYBOHLFMNOIUHHPHUL/action/citation_signature","submit_replication":"https://pith.science/pith/4E3JQNJZQYBOHLFMNOIUHHPHUL/action/replication_record"}},"created_at":"2026-05-18T00:22:12.709321+00:00","updated_at":"2026-05-18T00:22:12.709321+00:00"}