{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:VSB3RAKQ5FUGHNWRT6OISUHCU7","short_pith_number":"pith:VSB3RAKQ","schema_version":"1.0","canonical_sha256":"ac83b88150e96863b6d19f9c8950e2a7c0b812ef5006929e04ba50f207f632d1","source":{"kind":"arxiv","id":"1609.07887","version":1},"attestation_state":"computed","paper":{"title":"Interpretation of Compositional Regression with Application to Time Budget Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Eva Fiserova, Ivo Muller, Jana Vancakova, Jan Smahaj, Karel Hron, Panajotis Cakirpaloglu","submitted_at":"2016-09-26T08:58:38Z","abstract_excerpt":"Regression with compositional response or covariates, or even regression between parts of a composition, is frequently employed in social sciences. Among other possible applications, it may help to reveal interesting features in time allocation analysis. As individual activities represent relative contributions to the total amount of time, statistical processing of raw data (frequently represented directly as proportions or percentages) using standard methods may lead to biased results. Specific geometrical features of time budget variables are captured by the logratio methodology of compositi"},"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":"1609.07887","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"math.ST","submitted_at":"2016-09-26T08:58:38Z","cross_cats_sorted":["stat.ME","stat.TH"],"title_canon_sha256":"4fae02ca67853d4f5e23bac012a4f63a7e7b2a0bde7f91035d570eb78c3f54ef","abstract_canon_sha256":"d9cca6d9adf7307276ab06233b06ef61939bfb09d1cfe71e4bd0376ec73f1eca"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:03:55.136175Z","signature_b64":"uhh9RIPkwCsWg88ICIv9GyPBzrVLHnZW0vuDVHYFf7jNeKB3QnqI7Ge3B3qcsDpS4W7q4e9nDD4Qqx3ZzTxeCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ac83b88150e96863b6d19f9c8950e2a7c0b812ef5006929e04ba50f207f632d1","last_reissued_at":"2026-05-18T01:03:55.135582Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:03:55.135582Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Interpretation of Compositional Regression with Application to Time Budget Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ME","stat.TH"],"primary_cat":"math.ST","authors_text":"Eva Fiserova, Ivo Muller, Jana Vancakova, Jan Smahaj, Karel Hron, Panajotis Cakirpaloglu","submitted_at":"2016-09-26T08:58:38Z","abstract_excerpt":"Regression with compositional response or covariates, or even regression between parts of a composition, is frequently employed in social sciences. Among other possible applications, it may help to reveal interesting features in time allocation analysis. As individual activities represent relative contributions to the total amount of time, statistical processing of raw data (frequently represented directly as proportions or percentages) using standard methods may lead to biased results. Specific geometrical features of time budget variables are captured by the logratio methodology of compositi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1609.07887","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":"1609.07887","created_at":"2026-05-18T01:03:55.135697+00:00"},{"alias_kind":"arxiv_version","alias_value":"1609.07887v1","created_at":"2026-05-18T01:03:55.135697+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1609.07887","created_at":"2026-05-18T01:03:55.135697+00:00"},{"alias_kind":"pith_short_12","alias_value":"VSB3RAKQ5FUG","created_at":"2026-05-18T12:30:48.956258+00:00"},{"alias_kind":"pith_short_16","alias_value":"VSB3RAKQ5FUGHNWR","created_at":"2026-05-18T12:30:48.956258+00:00"},{"alias_kind":"pith_short_8","alias_value":"VSB3RAKQ","created_at":"2026-05-18T12:30:48.956258+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/VSB3RAKQ5FUGHNWRT6OISUHCU7","json":"https://pith.science/pith/VSB3RAKQ5FUGHNWRT6OISUHCU7.json","graph_json":"https://pith.science/api/pith-number/VSB3RAKQ5FUGHNWRT6OISUHCU7/graph.json","events_json":"https://pith.science/api/pith-number/VSB3RAKQ5FUGHNWRT6OISUHCU7/events.json","paper":"https://pith.science/paper/VSB3RAKQ"},"agent_actions":{"view_html":"https://pith.science/pith/VSB3RAKQ5FUGHNWRT6OISUHCU7","download_json":"https://pith.science/pith/VSB3RAKQ5FUGHNWRT6OISUHCU7.json","view_paper":"https://pith.science/paper/VSB3RAKQ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1609.07887&json=true","fetch_graph":"https://pith.science/api/pith-number/VSB3RAKQ5FUGHNWRT6OISUHCU7/graph.json","fetch_events":"https://pith.science/api/pith-number/VSB3RAKQ5FUGHNWRT6OISUHCU7/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VSB3RAKQ5FUGHNWRT6OISUHCU7/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VSB3RAKQ5FUGHNWRT6OISUHCU7/action/storage_attestation","attest_author":"https://pith.science/pith/VSB3RAKQ5FUGHNWRT6OISUHCU7/action/author_attestation","sign_citation":"https://pith.science/pith/VSB3RAKQ5FUGHNWRT6OISUHCU7/action/citation_signature","submit_replication":"https://pith.science/pith/VSB3RAKQ5FUGHNWRT6OISUHCU7/action/replication_record"}},"created_at":"2026-05-18T01:03:55.135697+00:00","updated_at":"2026-05-18T01:03:55.135697+00:00"}