{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2015:POZRDF6DEPQPNMJN7RHV4XUEBC","short_pith_number":"pith:POZRDF6D","schema_version":"1.0","canonical_sha256":"7bb31197c323e0f6b12dfc4f5e5e8408816fac3a441ad93a3196ea5ce20784f8","source":{"kind":"arxiv","id":"1506.04976","version":2},"attestation_state":"computed","paper":{"title":"Improved classification for compositional data using the $\\alpha$-transformation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Andrew T.A. Wood, Michail Tsagris, Simon Preston","submitted_at":"2015-06-16T13:59:49Z","abstract_excerpt":"In compositional data analysis an observation is a vector containing non-negative values, only the relative sizes of which are considered to be of interest. Without loss of generality, a compositional vector can be taken to be a vector of proportions that sum to one. Data of this type arise in many areas including geology, archaeology, biology, economics and political science. In this paper we investigate methods for classification of compositional data. Our approach centres on the idea of using the $\\alpha$-transformation to transform the data and then to classify the transformed data via reg"},"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":"1506.04976","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2015-06-16T13:59:49Z","cross_cats_sorted":[],"title_canon_sha256":"8affff7b7a4d6d563879a84544888545ba5e5943fa78e0eb815d924198899b20","abstract_canon_sha256":"0825ef7c4e1a609edd84062a5c894068e43a4f5bff369d3e6294334d45f31ea9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:46:13.039114Z","signature_b64":"GRICzToA0+k76TF+soqWBVq7VPKi2vJBuegMRxKe8/hNQwvZZwBWNjQwigCS7Zk8tmJ1YsAUoRWnGmQmUaxuDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7bb31197c323e0f6b12dfc4f5e5e8408816fac3a441ad93a3196ea5ce20784f8","last_reissued_at":"2026-05-18T01:46:13.038578Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:46:13.038578Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Improved classification for compositional data using the $\\alpha$-transformation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Andrew T.A. Wood, Michail Tsagris, Simon Preston","submitted_at":"2015-06-16T13:59:49Z","abstract_excerpt":"In compositional data analysis an observation is a vector containing non-negative values, only the relative sizes of which are considered to be of interest. Without loss of generality, a compositional vector can be taken to be a vector of proportions that sum to one. Data of this type arise in many areas including geology, archaeology, biology, economics and political science. In this paper we investigate methods for classification of compositional data. Our approach centres on the idea of using the $\\alpha$-transformation to transform the data and then to classify the transformed data via reg"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1506.04976","kind":"arxiv","version":2},"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":"1506.04976","created_at":"2026-05-18T01:46:13.038677+00:00"},{"alias_kind":"arxiv_version","alias_value":"1506.04976v2","created_at":"2026-05-18T01:46:13.038677+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1506.04976","created_at":"2026-05-18T01:46:13.038677+00:00"},{"alias_kind":"pith_short_12","alias_value":"POZRDF6DEPQP","created_at":"2026-05-18T12:29:37.295048+00:00"},{"alias_kind":"pith_short_16","alias_value":"POZRDF6DEPQPNMJN","created_at":"2026-05-18T12:29:37.295048+00:00"},{"alias_kind":"pith_short_8","alias_value":"POZRDF6D","created_at":"2026-05-18T12:29:37.295048+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/POZRDF6DEPQPNMJN7RHV4XUEBC","json":"https://pith.science/pith/POZRDF6DEPQPNMJN7RHV4XUEBC.json","graph_json":"https://pith.science/api/pith-number/POZRDF6DEPQPNMJN7RHV4XUEBC/graph.json","events_json":"https://pith.science/api/pith-number/POZRDF6DEPQPNMJN7RHV4XUEBC/events.json","paper":"https://pith.science/paper/POZRDF6D"},"agent_actions":{"view_html":"https://pith.science/pith/POZRDF6DEPQPNMJN7RHV4XUEBC","download_json":"https://pith.science/pith/POZRDF6DEPQPNMJN7RHV4XUEBC.json","view_paper":"https://pith.science/paper/POZRDF6D","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1506.04976&json=true","fetch_graph":"https://pith.science/api/pith-number/POZRDF6DEPQPNMJN7RHV4XUEBC/graph.json","fetch_events":"https://pith.science/api/pith-number/POZRDF6DEPQPNMJN7RHV4XUEBC/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/POZRDF6DEPQPNMJN7RHV4XUEBC/action/timestamp_anchor","attest_storage":"https://pith.science/pith/POZRDF6DEPQPNMJN7RHV4XUEBC/action/storage_attestation","attest_author":"https://pith.science/pith/POZRDF6DEPQPNMJN7RHV4XUEBC/action/author_attestation","sign_citation":"https://pith.science/pith/POZRDF6DEPQPNMJN7RHV4XUEBC/action/citation_signature","submit_replication":"https://pith.science/pith/POZRDF6DEPQPNMJN7RHV4XUEBC/action/replication_record"}},"created_at":"2026-05-18T01:46:13.038677+00:00","updated_at":"2026-05-18T01:46:13.038677+00:00"}