{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:2EBVCA2RT4KDLFEB45WGLPEP6R","short_pith_number":"pith:2EBVCA2R","schema_version":"1.0","canonical_sha256":"d1035103519f14359481e76c65bc8ff4431ace69470a2ddc23ab4e5595cac612","source":{"kind":"arxiv","id":"2606.22403","version":1},"attestation_state":"computed","paper":{"title":"Maximum Likelihood Criterion for Non-nested Model Selection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Min Tsao","submitted_at":"2026-06-21T09:18:46Z","abstract_excerpt":"Penalization is a widely used approach to model selection with roots in information theory and Bayesian inference. We study a model selection problem involving non-nested candidate models for which penalization is counterproductive. We propose a Maximum Likelihood Criterion for this non-nested setting that selects the candidate model with the highest maximum likelihood. This criterion does not take into consideration the number of parameters of a candidate model. It is well-suited for situations where all candidate models are regarded as equal with no preference for models having fewer paramet"},"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":"2606.22403","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ME","submitted_at":"2026-06-21T09:18:46Z","cross_cats_sorted":[],"title_canon_sha256":"6bfdac1983e2239c088ac697a215c7190626e062cdc88453f0aa16bb5b262a91","abstract_canon_sha256":"2270ce03c6966b7f31aa98dbb4a138e63b4ff59506be949bfeae5585448b26bb"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-23T02:13:37.500147Z","signature_b64":"FMuuIpBtr9GjWp8sqGT/XOF9cT//TM36EnM/xGLoekoDPBEY0WAFrNXKE2BBHgX9T7oSxgcKO8cr7KF4Dl7rDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d1035103519f14359481e76c65bc8ff4431ace69470a2ddc23ab4e5595cac612","last_reissued_at":"2026-06-23T02:13:37.499745Z","signature_status":"signed_v1","first_computed_at":"2026-06-23T02:13:37.499745Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Maximum Likelihood Criterion for Non-nested Model Selection","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Min Tsao","submitted_at":"2026-06-21T09:18:46Z","abstract_excerpt":"Penalization is a widely used approach to model selection with roots in information theory and Bayesian inference. We study a model selection problem involving non-nested candidate models for which penalization is counterproductive. We propose a Maximum Likelihood Criterion for this non-nested setting that selects the candidate model with the highest maximum likelihood. This criterion does not take into consideration the number of parameters of a candidate model. It is well-suited for situations where all candidate models are regarded as equal with no preference for models having fewer paramet"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.22403","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2606.22403/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"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":"2606.22403","created_at":"2026-06-23T02:13:37.499806+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.22403v1","created_at":"2026-06-23T02:13:37.499806+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.22403","created_at":"2026-06-23T02:13:37.499806+00:00"},{"alias_kind":"pith_short_12","alias_value":"2EBVCA2RT4KD","created_at":"2026-06-23T02:13:37.499806+00:00"},{"alias_kind":"pith_short_16","alias_value":"2EBVCA2RT4KDLFEB","created_at":"2026-06-23T02:13:37.499806+00:00"},{"alias_kind":"pith_short_8","alias_value":"2EBVCA2R","created_at":"2026-06-23T02:13:37.499806+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/2EBVCA2RT4KDLFEB45WGLPEP6R","json":"https://pith.science/pith/2EBVCA2RT4KDLFEB45WGLPEP6R.json","graph_json":"https://pith.science/api/pith-number/2EBVCA2RT4KDLFEB45WGLPEP6R/graph.json","events_json":"https://pith.science/api/pith-number/2EBVCA2RT4KDLFEB45WGLPEP6R/events.json","paper":"https://pith.science/paper/2EBVCA2R"},"agent_actions":{"view_html":"https://pith.science/pith/2EBVCA2RT4KDLFEB45WGLPEP6R","download_json":"https://pith.science/pith/2EBVCA2RT4KDLFEB45WGLPEP6R.json","view_paper":"https://pith.science/paper/2EBVCA2R","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.22403&json=true","fetch_graph":"https://pith.science/api/pith-number/2EBVCA2RT4KDLFEB45WGLPEP6R/graph.json","fetch_events":"https://pith.science/api/pith-number/2EBVCA2RT4KDLFEB45WGLPEP6R/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2EBVCA2RT4KDLFEB45WGLPEP6R/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2EBVCA2RT4KDLFEB45WGLPEP6R/action/storage_attestation","attest_author":"https://pith.science/pith/2EBVCA2RT4KDLFEB45WGLPEP6R/action/author_attestation","sign_citation":"https://pith.science/pith/2EBVCA2RT4KDLFEB45WGLPEP6R/action/citation_signature","submit_replication":"https://pith.science/pith/2EBVCA2RT4KDLFEB45WGLPEP6R/action/replication_record"}},"created_at":"2026-06-23T02:13:37.499806+00:00","updated_at":"2026-06-23T02:13:37.499806+00:00"}