{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2009:JJIJLOJOKYVXOFFUIQWDZCR5BM","short_pith_number":"pith:JJIJLOJO","schema_version":"1.0","canonical_sha256":"4a5095b92e562b7714b4442c3c8a3d0b1ab2d9ef0f56b205f1f6d021c3991109","source":{"kind":"arxiv","id":"0906.3215","version":1},"attestation_state":"computed","paper":{"title":"Reduction algorithm for the NPMLE for the distribution function of bivariate interval censored data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Marloes H. Maathuis","submitted_at":"2009-06-17T15:29:16Z","abstract_excerpt":"We study computational aspects of the nonparametric maximum likelihood estimator (NPMLE) for the distribution function of bivariate interval censored data. The computation of the NPMLE consists of two steps: a parameter reduction step and an optimization step. In this paper we focus on the reduction step. We introduce two new reduction algorithms: the Tree algorithm and the HeightMap algorithm. The Tree algorithm is only mentioned briefly. The HeightMap algorithm is discussed in detail and also given in pseudo code. It is a very fast and simple algorithm of time complexity O(n^2). This is an o"},"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":"0906.3215","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.CO","submitted_at":"2009-06-17T15:29:16Z","cross_cats_sorted":[],"title_canon_sha256":"e759d267e5be03abca6f53666ce1ee292c4b348a96c0a94e25b187dcc2ef5ddd","abstract_canon_sha256":"ca00f3d53b7e821afb51dc1746bec38a29c6da82ff7dc36fef2fe25196da58ad"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T04:04:53.653172Z","signature_b64":"g6tIpLGPpEHePn1Dhgos0qyepYV1o6LSDKbulS5C6Wsahpt5Bt5PVXPhHrXzRGkSFKVPHYpRRtbJRa4ODEL3Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4a5095b92e562b7714b4442c3c8a3d0b1ab2d9ef0f56b205f1f6d021c3991109","last_reissued_at":"2026-05-18T04:04:53.652620Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T04:04:53.652620Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Reduction algorithm for the NPMLE for the distribution function of bivariate interval censored data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.CO","authors_text":"Marloes H. Maathuis","submitted_at":"2009-06-17T15:29:16Z","abstract_excerpt":"We study computational aspects of the nonparametric maximum likelihood estimator (NPMLE) for the distribution function of bivariate interval censored data. The computation of the NPMLE consists of two steps: a parameter reduction step and an optimization step. In this paper we focus on the reduction step. We introduce two new reduction algorithms: the Tree algorithm and the HeightMap algorithm. The Tree algorithm is only mentioned briefly. The HeightMap algorithm is discussed in detail and also given in pseudo code. It is a very fast and simple algorithm of time complexity O(n^2). This is an o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"0906.3215","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":"0906.3215","created_at":"2026-05-18T04:04:53.652719+00:00"},{"alias_kind":"arxiv_version","alias_value":"0906.3215v1","created_at":"2026-05-18T04:04:53.652719+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.0906.3215","created_at":"2026-05-18T04:04:53.652719+00:00"},{"alias_kind":"pith_short_12","alias_value":"JJIJLOJOKYVX","created_at":"2026-05-18T12:26:00.592388+00:00"},{"alias_kind":"pith_short_16","alias_value":"JJIJLOJOKYVXOFFU","created_at":"2026-05-18T12:26:00.592388+00:00"},{"alias_kind":"pith_short_8","alias_value":"JJIJLOJO","created_at":"2026-05-18T12:26:00.592388+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/JJIJLOJOKYVXOFFUIQWDZCR5BM","json":"https://pith.science/pith/JJIJLOJOKYVXOFFUIQWDZCR5BM.json","graph_json":"https://pith.science/api/pith-number/JJIJLOJOKYVXOFFUIQWDZCR5BM/graph.json","events_json":"https://pith.science/api/pith-number/JJIJLOJOKYVXOFFUIQWDZCR5BM/events.json","paper":"https://pith.science/paper/JJIJLOJO"},"agent_actions":{"view_html":"https://pith.science/pith/JJIJLOJOKYVXOFFUIQWDZCR5BM","download_json":"https://pith.science/pith/JJIJLOJOKYVXOFFUIQWDZCR5BM.json","view_paper":"https://pith.science/paper/JJIJLOJO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=0906.3215&json=true","fetch_graph":"https://pith.science/api/pith-number/JJIJLOJOKYVXOFFUIQWDZCR5BM/graph.json","fetch_events":"https://pith.science/api/pith-number/JJIJLOJOKYVXOFFUIQWDZCR5BM/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/JJIJLOJOKYVXOFFUIQWDZCR5BM/action/timestamp_anchor","attest_storage":"https://pith.science/pith/JJIJLOJOKYVXOFFUIQWDZCR5BM/action/storage_attestation","attest_author":"https://pith.science/pith/JJIJLOJOKYVXOFFUIQWDZCR5BM/action/author_attestation","sign_citation":"https://pith.science/pith/JJIJLOJOKYVXOFFUIQWDZCR5BM/action/citation_signature","submit_replication":"https://pith.science/pith/JJIJLOJOKYVXOFFUIQWDZCR5BM/action/replication_record"}},"created_at":"2026-05-18T04:04:53.652719+00:00","updated_at":"2026-05-18T04:04:53.652719+00:00"}