{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2012:355SZ75KAHDSCQYJQ6GZ3XMNHT","short_pith_number":"pith:355SZ75K","schema_version":"1.0","canonical_sha256":"df7b2cffaa01c7214309878d9ddd8d3cc3daed654dcbbce4731f6aaa3ebb785d","source":{"kind":"arxiv","id":"1212.1949","version":4},"attestation_state":"computed","paper":{"title":"A Semiparametric Bayesian Approach for Extreme Values Using Dirichlet Process Mixture of Gamma and Generalized Pareto Densities","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ME","authors_text":"Jairo Fuquene","submitted_at":"2012-12-10T01:38:19Z","abstract_excerpt":"For extreme value estimation we propose to use a model with a Dirichlet process mixture of gamma densities in the center and generalized Pareto densities for the tails. Due to the randomness in the center and a heavy tailed density in the tails density estimation and posterior inference for high quantiles are possible. The approach can be used in a \"default\" manner on the positive reals because it works when prior information is unavailable. The proposed model can be easy to implement and a sensitivity analysis is provided. We applied the proposed model for simulated and real data sets."},"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":"1212.1949","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2012-12-10T01:38:19Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"4dc741647dbf13b39d9b42b70d8a97a166150f139967f1c28ec40a4b659c3b3c","abstract_canon_sha256":"64adb71a36539195947db825ccae2e83607ae96f7797d974a47f22b1c4f51386"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T03:29:29.449196Z","signature_b64":"u2XHJK6mMUl6g+q4JKU3izOKQdN7rEkogBKxyd+HcimkH/KK4tzHBfFoBIhgTmUaZNnj30yyz0SGmU2sArsXCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"df7b2cffaa01c7214309878d9ddd8d3cc3daed654dcbbce4731f6aaa3ebb785d","last_reissued_at":"2026-05-18T03:29:29.448491Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T03:29:29.448491Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Semiparametric Bayesian Approach for Extreme Values Using Dirichlet Process Mixture of Gamma and Generalized Pareto Densities","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["math.ST","stat.TH"],"primary_cat":"stat.ME","authors_text":"Jairo Fuquene","submitted_at":"2012-12-10T01:38:19Z","abstract_excerpt":"For extreme value estimation we propose to use a model with a Dirichlet process mixture of gamma densities in the center and generalized Pareto densities for the tails. Due to the randomness in the center and a heavy tailed density in the tails density estimation and posterior inference for high quantiles are possible. The approach can be used in a \"default\" manner on the positive reals because it works when prior information is unavailable. The proposed model can be easy to implement and a sensitivity analysis is provided. We applied the proposed model for simulated and real data sets."},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1212.1949","kind":"arxiv","version":4},"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":"1212.1949","created_at":"2026-05-18T03:29:29.448612+00:00"},{"alias_kind":"arxiv_version","alias_value":"1212.1949v4","created_at":"2026-05-18T03:29:29.448612+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1212.1949","created_at":"2026-05-18T03:29:29.448612+00:00"},{"alias_kind":"pith_short_12","alias_value":"355SZ75KAHDS","created_at":"2026-05-18T12:26:50.516681+00:00"},{"alias_kind":"pith_short_16","alias_value":"355SZ75KAHDSCQYJ","created_at":"2026-05-18T12:26:50.516681+00:00"},{"alias_kind":"pith_short_8","alias_value":"355SZ75K","created_at":"2026-05-18T12:26:50.516681+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/355SZ75KAHDSCQYJQ6GZ3XMNHT","json":"https://pith.science/pith/355SZ75KAHDSCQYJQ6GZ3XMNHT.json","graph_json":"https://pith.science/api/pith-number/355SZ75KAHDSCQYJQ6GZ3XMNHT/graph.json","events_json":"https://pith.science/api/pith-number/355SZ75KAHDSCQYJQ6GZ3XMNHT/events.json","paper":"https://pith.science/paper/355SZ75K"},"agent_actions":{"view_html":"https://pith.science/pith/355SZ75KAHDSCQYJQ6GZ3XMNHT","download_json":"https://pith.science/pith/355SZ75KAHDSCQYJQ6GZ3XMNHT.json","view_paper":"https://pith.science/paper/355SZ75K","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1212.1949&json=true","fetch_graph":"https://pith.science/api/pith-number/355SZ75KAHDSCQYJQ6GZ3XMNHT/graph.json","fetch_events":"https://pith.science/api/pith-number/355SZ75KAHDSCQYJQ6GZ3XMNHT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/355SZ75KAHDSCQYJQ6GZ3XMNHT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/355SZ75KAHDSCQYJQ6GZ3XMNHT/action/storage_attestation","attest_author":"https://pith.science/pith/355SZ75KAHDSCQYJQ6GZ3XMNHT/action/author_attestation","sign_citation":"https://pith.science/pith/355SZ75KAHDSCQYJQ6GZ3XMNHT/action/citation_signature","submit_replication":"https://pith.science/pith/355SZ75KAHDSCQYJQ6GZ3XMNHT/action/replication_record"}},"created_at":"2026-05-18T03:29:29.448612+00:00","updated_at":"2026-05-18T03:29:29.448612+00:00"}