{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2018:IXV3SMMTMTGABDQCZXYXJI7HAT","short_pith_number":"pith:IXV3SMMT","schema_version":"1.0","canonical_sha256":"45ebb9319364cc008e02cdf174a3e704d40a3b3e00583bb56cef8656916b096f","source":{"kind":"arxiv","id":"1804.10820","version":1},"attestation_state":"computed","paper":{"title":"On a bivariate Birnbaum-Saunders distribution parameterized by its means: features, reliability analysis and application","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Helton Saulo, Jeremias Le\\~ao, Roberto Vila, Vera Tomazella, Victor Leiva","submitted_at":"2018-04-28T15:25:34Z","abstract_excerpt":"Birnbaum-Saunders models have been widely used to model positively skewed data. In this paper, we introduce a bivariate Birnbaum-Saunders distribution which has the means as parameters. We present some properties of the univariate and bivariate Birnbaum-Saunders models. We discuss the maximum likelihood and modified moment estimation of the model parameters and associated inference. A simulation study is conducted to evaluate the performance of the maximum likelihood and modified moment estimators. The probability coverages of confidence intervals are also discussed. Finally, a real-world data"},"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":"1804.10820","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2018-04-28T15:25:34Z","cross_cats_sorted":[],"title_canon_sha256":"161b03f2303e509e13212f1464840f38a947a9ed1f90b611dbabd5e92a3257bb","abstract_canon_sha256":"1fc5f0fd261eebff9909423bb29b48a5f5e6a7e50bf8b3317144ad8799b16f40"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:17:15.431787Z","signature_b64":"Ibq3iwLZxyq9EXAl1halLZ+yJON8XXHmDPxXFmJJCK75cLLxuHdANNzG3iJY1iUHDPhKApzpiaSV0K9SiwNuAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"45ebb9319364cc008e02cdf174a3e704d40a3b3e00583bb56cef8656916b096f","last_reissued_at":"2026-05-18T00:17:15.431105Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:17:15.431105Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"On a bivariate Birnbaum-Saunders distribution parameterized by its means: features, reliability analysis and application","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Helton Saulo, Jeremias Le\\~ao, Roberto Vila, Vera Tomazella, Victor Leiva","submitted_at":"2018-04-28T15:25:34Z","abstract_excerpt":"Birnbaum-Saunders models have been widely used to model positively skewed data. In this paper, we introduce a bivariate Birnbaum-Saunders distribution which has the means as parameters. We present some properties of the univariate and bivariate Birnbaum-Saunders models. We discuss the maximum likelihood and modified moment estimation of the model parameters and associated inference. A simulation study is conducted to evaluate the performance of the maximum likelihood and modified moment estimators. The probability coverages of confidence intervals are also discussed. Finally, a real-world data"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1804.10820","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":"1804.10820","created_at":"2026-05-18T00:17:15.431196+00:00"},{"alias_kind":"arxiv_version","alias_value":"1804.10820v1","created_at":"2026-05-18T00:17:15.431196+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1804.10820","created_at":"2026-05-18T00:17:15.431196+00:00"},{"alias_kind":"pith_short_12","alias_value":"IXV3SMMTMTGA","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_16","alias_value":"IXV3SMMTMTGABDQC","created_at":"2026-05-18T12:32:31.084164+00:00"},{"alias_kind":"pith_short_8","alias_value":"IXV3SMMT","created_at":"2026-05-18T12:32:31.084164+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/IXV3SMMTMTGABDQCZXYXJI7HAT","json":"https://pith.science/pith/IXV3SMMTMTGABDQCZXYXJI7HAT.json","graph_json":"https://pith.science/api/pith-number/IXV3SMMTMTGABDQCZXYXJI7HAT/graph.json","events_json":"https://pith.science/api/pith-number/IXV3SMMTMTGABDQCZXYXJI7HAT/events.json","paper":"https://pith.science/paper/IXV3SMMT"},"agent_actions":{"view_html":"https://pith.science/pith/IXV3SMMTMTGABDQCZXYXJI7HAT","download_json":"https://pith.science/pith/IXV3SMMTMTGABDQCZXYXJI7HAT.json","view_paper":"https://pith.science/paper/IXV3SMMT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1804.10820&json=true","fetch_graph":"https://pith.science/api/pith-number/IXV3SMMTMTGABDQCZXYXJI7HAT/graph.json","fetch_events":"https://pith.science/api/pith-number/IXV3SMMTMTGABDQCZXYXJI7HAT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IXV3SMMTMTGABDQCZXYXJI7HAT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IXV3SMMTMTGABDQCZXYXJI7HAT/action/storage_attestation","attest_author":"https://pith.science/pith/IXV3SMMTMTGABDQCZXYXJI7HAT/action/author_attestation","sign_citation":"https://pith.science/pith/IXV3SMMTMTGABDQCZXYXJI7HAT/action/citation_signature","submit_replication":"https://pith.science/pith/IXV3SMMTMTGABDQCZXYXJI7HAT/action/replication_record"}},"created_at":"2026-05-18T00:17:15.431196+00:00","updated_at":"2026-05-18T00:17:15.431196+00:00"}