{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:UIUPDZLMQNUMPSQIIFVU253G2Y","short_pith_number":"pith:UIUPDZLM","schema_version":"1.0","canonical_sha256":"a228f1e56c8368c7ca08416b4d7766d63f6f6ce4ff6cffd993b7a40ece74e1f1","source":{"kind":"arxiv","id":"2506.20425","version":3},"attestation_state":"computed","paper":{"title":"Scalable Subset Selection in Linear Mixed Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.CO","stat.ME"],"primary_cat":"stat.ML","authors_text":"Joanna J. J. Wang, Matt P. Wand, Ryan Thompson","submitted_at":"2025-06-25T13:39:30Z","abstract_excerpt":"Linear mixed models (LMMs), which incorporate fixed and random effects, are key tools for analyzing heterogeneous data, such as in personalized medicine. Nowadays, this type of data is increasingly wide, sometimes containing thousands of candidate predictors, necessitating sparsity for prediction and interpretation. However, existing sparse learning methods for LMMs do not scale well beyond tens or hundreds of predictors, leaving a large gap compared with sparse methods for linear models, which ignore random effects. This paper closes the gap with a new $\\ell_0$ regularized method for LMM subs"},"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":"2506.20425","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2025-06-25T13:39:30Z","cross_cats_sorted":["cs.LG","stat.CO","stat.ME"],"title_canon_sha256":"5287bb08870cb7fddf52fd1bf4ee732138141dac641d6bb630b48b5cbab1cec4","abstract_canon_sha256":"89af2a746dc3cd713e1e4b117376170c6aef991cb010db9e2de7df2dd0562e01"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:17.868916Z","signature_b64":"Y56Qf95mrBidbSgUFCVcCMiO337V5lAD9SeA/PKBKl1Vk5kip66g9bF/BKjqGa9j//SCLonfcFtUoVQh4+sOCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a228f1e56c8368c7ca08416b4d7766d63f6f6ce4ff6cffd993b7a40ece74e1f1","last_reissued_at":"2026-05-17T23:39:17.868291Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:17.868291Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Scalable Subset Selection in Linear Mixed Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.CO","stat.ME"],"primary_cat":"stat.ML","authors_text":"Joanna J. J. Wang, Matt P. Wand, Ryan Thompson","submitted_at":"2025-06-25T13:39:30Z","abstract_excerpt":"Linear mixed models (LMMs), which incorporate fixed and random effects, are key tools for analyzing heterogeneous data, such as in personalized medicine. Nowadays, this type of data is increasingly wide, sometimes containing thousands of candidate predictors, necessitating sparsity for prediction and interpretation. However, existing sparse learning methods for LMMs do not scale well beyond tens or hundreds of predictors, leaving a large gap compared with sparse methods for linear models, which ignore random effects. This paper closes the gap with a new $\\ell_0$ regularized method for LMM subs"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.20425","kind":"arxiv","version":3},"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":"2506.20425","created_at":"2026-05-17T23:39:17.868399+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.20425v3","created_at":"2026-05-17T23:39:17.868399+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.20425","created_at":"2026-05-17T23:39:17.868399+00:00"},{"alias_kind":"pith_short_12","alias_value":"UIUPDZLMQNUM","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_16","alias_value":"UIUPDZLMQNUMPSQI","created_at":"2026-05-18T12:33:37.589309+00:00"},{"alias_kind":"pith_short_8","alias_value":"UIUPDZLM","created_at":"2026-05-18T12:33:37.589309+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/UIUPDZLMQNUMPSQIIFVU253G2Y","json":"https://pith.science/pith/UIUPDZLMQNUMPSQIIFVU253G2Y.json","graph_json":"https://pith.science/api/pith-number/UIUPDZLMQNUMPSQIIFVU253G2Y/graph.json","events_json":"https://pith.science/api/pith-number/UIUPDZLMQNUMPSQIIFVU253G2Y/events.json","paper":"https://pith.science/paper/UIUPDZLM"},"agent_actions":{"view_html":"https://pith.science/pith/UIUPDZLMQNUMPSQIIFVU253G2Y","download_json":"https://pith.science/pith/UIUPDZLMQNUMPSQIIFVU253G2Y.json","view_paper":"https://pith.science/paper/UIUPDZLM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.20425&json=true","fetch_graph":"https://pith.science/api/pith-number/UIUPDZLMQNUMPSQIIFVU253G2Y/graph.json","fetch_events":"https://pith.science/api/pith-number/UIUPDZLMQNUMPSQIIFVU253G2Y/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UIUPDZLMQNUMPSQIIFVU253G2Y/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UIUPDZLMQNUMPSQIIFVU253G2Y/action/storage_attestation","attest_author":"https://pith.science/pith/UIUPDZLMQNUMPSQIIFVU253G2Y/action/author_attestation","sign_citation":"https://pith.science/pith/UIUPDZLMQNUMPSQIIFVU253G2Y/action/citation_signature","submit_replication":"https://pith.science/pith/UIUPDZLMQNUMPSQIIFVU253G2Y/action/replication_record"}},"created_at":"2026-05-17T23:39:17.868399+00:00","updated_at":"2026-05-17T23:39:17.868399+00:00"}