{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:RZYA7CIN44OAC67D6JTIB2BD7N","short_pith_number":"pith:RZYA7CIN","schema_version":"1.0","canonical_sha256":"8e700f890de71c017be3f26680e823fb6efca5c1d1de6ab44dd8191cc6a09b56","source":{"kind":"arxiv","id":"2607.17654","version":1},"attestation_state":"computed","paper":{"title":"An efficient adaptive dimension selection algorithm for multidimensional probit graded response models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ME"],"primary_cat":"stat.ML","authors_text":"Bin Lv, Meng Gao, Yincai Tang, Yu Zhou","submitted_at":"2026-07-20T08:06:47Z","abstract_excerpt":"Multidimensional graded response models (MGRMs) are widely used for analyzing ordinal questionnaire data in psychological and educational assessments. A central challenge in applying these models is determining the number of latent dimensions. Conventional approaches usually fit multiple fixed-dimensional models and select among them using post-hoc criteria such as AIC, BIC, or cross-validation, which can be computationally demanding and ignore uncertainty in dimensionality during estimation. We develop an adaptive Bayesian dimension selection framework for probit MGRMs. Building on the cumula"},"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":"2607.17654","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2026-07-20T08:06:47Z","cross_cats_sorted":["cs.LG","stat.ME"],"title_canon_sha256":"42394b349a813da8b2dbde58debd9f6acd344d4aae22c51bdca1bcc20113ef5c","abstract_canon_sha256":"c5dbc69d78baeca08cda0b7c136285fcb12781f90129fbb76c4f0bd666fc73c5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-21T02:21:53.080249Z","signature_b64":"bVwoAUh9MWp5RVCsaPDAMIKppvwpi/vrCv/DPa6gf+x/EGAEct83vigqr7jeey4gYSQwwsOskZAZF49A5o34Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8e700f890de71c017be3f26680e823fb6efca5c1d1de6ab44dd8191cc6a09b56","last_reissued_at":"2026-07-21T02:21:53.079305Z","signature_status":"signed_v1","first_computed_at":"2026-07-21T02:21:53.079305Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"An efficient adaptive dimension selection algorithm for multidimensional probit graded response models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ME"],"primary_cat":"stat.ML","authors_text":"Bin Lv, Meng Gao, Yincai Tang, Yu Zhou","submitted_at":"2026-07-20T08:06:47Z","abstract_excerpt":"Multidimensional graded response models (MGRMs) are widely used for analyzing ordinal questionnaire data in psychological and educational assessments. A central challenge in applying these models is determining the number of latent dimensions. Conventional approaches usually fit multiple fixed-dimensional models and select among them using post-hoc criteria such as AIC, BIC, or cross-validation, which can be computationally demanding and ignore uncertainty in dimensionality during estimation. We develop an adaptive Bayesian dimension selection framework for probit MGRMs. Building on the cumula"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.17654","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/2607.17654/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":"2607.17654","created_at":"2026-07-21T02:21:53.079750+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.17654v1","created_at":"2026-07-21T02:21:53.079750+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.17654","created_at":"2026-07-21T02:21:53.079750+00:00"},{"alias_kind":"pith_short_12","alias_value":"RZYA7CIN44OA","created_at":"2026-07-21T02:21:53.079750+00:00"},{"alias_kind":"pith_short_16","alias_value":"RZYA7CIN44OAC67D","created_at":"2026-07-21T02:21:53.079750+00:00"},{"alias_kind":"pith_short_8","alias_value":"RZYA7CIN","created_at":"2026-07-21T02:21:53.079750+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/RZYA7CIN44OAC67D6JTIB2BD7N","json":"https://pith.science/pith/RZYA7CIN44OAC67D6JTIB2BD7N.json","graph_json":"https://pith.science/api/pith-number/RZYA7CIN44OAC67D6JTIB2BD7N/graph.json","events_json":"https://pith.science/api/pith-number/RZYA7CIN44OAC67D6JTIB2BD7N/events.json","paper":"https://pith.science/paper/RZYA7CIN"},"agent_actions":{"view_html":"https://pith.science/pith/RZYA7CIN44OAC67D6JTIB2BD7N","download_json":"https://pith.science/pith/RZYA7CIN44OAC67D6JTIB2BD7N.json","view_paper":"https://pith.science/paper/RZYA7CIN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.17654&json=true","fetch_graph":"https://pith.science/api/pith-number/RZYA7CIN44OAC67D6JTIB2BD7N/graph.json","fetch_events":"https://pith.science/api/pith-number/RZYA7CIN44OAC67D6JTIB2BD7N/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RZYA7CIN44OAC67D6JTIB2BD7N/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RZYA7CIN44OAC67D6JTIB2BD7N/action/storage_attestation","attest_author":"https://pith.science/pith/RZYA7CIN44OAC67D6JTIB2BD7N/action/author_attestation","sign_citation":"https://pith.science/pith/RZYA7CIN44OAC67D6JTIB2BD7N/action/citation_signature","submit_replication":"https://pith.science/pith/RZYA7CIN44OAC67D6JTIB2BD7N/action/replication_record"}},"created_at":"2026-07-21T02:21:53.079750+00:00","updated_at":"2026-07-21T02:21:53.079750+00:00"}