{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2016:MADU3RG2KRKI5LU7FPT5B6YVEI","short_pith_number":"pith:MADU3RG2","schema_version":"1.0","canonical_sha256":"60074dc4da54548eae9f2be7d0fb15222396fa1163d6f03fb00644c056c23147","source":{"kind":"arxiv","id":"1608.00107","version":2},"attestation_state":"computed","paper":{"title":"Constructing Likelihood Functions for Interval-valued Random Variables","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ME","authors_text":"Boris Beranger, Scott A. Sisson, Xin Zhang","submitted_at":"2016-07-30T12:03:33Z","abstract_excerpt":"There is a growing need for the ability to analyse interval-valued data. However, existing descriptive frameworks to achieve this ignore the process by which interval-valued data are typically constructed; namely by the aggregation of real-valued data generated from some underlying process. In this article we develop the foundations of likelihood based statistical inference for random intervals that directly incorporates the underlying generative procedure into the analysis. That is, it permits the direct fitting of models for the underlying real-valued data given only the random interval-valu"},"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":"1608.00107","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2016-07-30T12:03:33Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"3966c50ef945a1924c2bf50a936e2c2cd99fd8f08330b45aa427acc23a2414aa","abstract_canon_sha256":"241af00d3d6f0efdbd42defa19cde8c2b0e2b505d6f4545088da597c93c857bf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:51:54.996108Z","signature_b64":"RV1MkCXKZD/jCaiq+y/wIxXTxltE5GhRQBtt+WXSDQ/OZuVqaEaK+AHl/5HBtm1AAFVTfe8Oc0HCFEX7HmhMBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"60074dc4da54548eae9f2be7d0fb15222396fa1163d6f03fb00644c056c23147","last_reissued_at":"2026-05-17T23:51:54.995559Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:51:54.995559Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Constructing Likelihood Functions for Interval-valued Random Variables","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ME","authors_text":"Boris Beranger, Scott A. Sisson, Xin Zhang","submitted_at":"2016-07-30T12:03:33Z","abstract_excerpt":"There is a growing need for the ability to analyse interval-valued data. However, existing descriptive frameworks to achieve this ignore the process by which interval-valued data are typically constructed; namely by the aggregation of real-valued data generated from some underlying process. In this article we develop the foundations of likelihood based statistical inference for random intervals that directly incorporates the underlying generative procedure into the analysis. That is, it permits the direct fitting of models for the underlying real-valued data given only the random interval-valu"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.00107","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":"1608.00107","created_at":"2026-05-17T23:51:54.995657+00:00"},{"alias_kind":"arxiv_version","alias_value":"1608.00107v2","created_at":"2026-05-17T23:51:54.995657+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.00107","created_at":"2026-05-17T23:51:54.995657+00:00"},{"alias_kind":"pith_short_12","alias_value":"MADU3RG2KRKI","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_16","alias_value":"MADU3RG2KRKI5LU7","created_at":"2026-05-18T12:30:32.724797+00:00"},{"alias_kind":"pith_short_8","alias_value":"MADU3RG2","created_at":"2026-05-18T12:30:32.724797+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/MADU3RG2KRKI5LU7FPT5B6YVEI","json":"https://pith.science/pith/MADU3RG2KRKI5LU7FPT5B6YVEI.json","graph_json":"https://pith.science/api/pith-number/MADU3RG2KRKI5LU7FPT5B6YVEI/graph.json","events_json":"https://pith.science/api/pith-number/MADU3RG2KRKI5LU7FPT5B6YVEI/events.json","paper":"https://pith.science/paper/MADU3RG2"},"agent_actions":{"view_html":"https://pith.science/pith/MADU3RG2KRKI5LU7FPT5B6YVEI","download_json":"https://pith.science/pith/MADU3RG2KRKI5LU7FPT5B6YVEI.json","view_paper":"https://pith.science/paper/MADU3RG2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1608.00107&json=true","fetch_graph":"https://pith.science/api/pith-number/MADU3RG2KRKI5LU7FPT5B6YVEI/graph.json","fetch_events":"https://pith.science/api/pith-number/MADU3RG2KRKI5LU7FPT5B6YVEI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MADU3RG2KRKI5LU7FPT5B6YVEI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MADU3RG2KRKI5LU7FPT5B6YVEI/action/storage_attestation","attest_author":"https://pith.science/pith/MADU3RG2KRKI5LU7FPT5B6YVEI/action/author_attestation","sign_citation":"https://pith.science/pith/MADU3RG2KRKI5LU7FPT5B6YVEI/action/citation_signature","submit_replication":"https://pith.science/pith/MADU3RG2KRKI5LU7FPT5B6YVEI/action/replication_record"}},"created_at":"2026-05-17T23:51:54.995657+00:00","updated_at":"2026-05-17T23:51:54.995657+00:00"}