{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:Y3PUEHG33HXM55FZ4XW4GGK5GG","short_pith_number":"pith:Y3PUEHG3","schema_version":"1.0","canonical_sha256":"c6df421cdbd9eecef4b9e5edc3195d31b056e39b2dd09bd758c1cef4c1c00d15","source":{"kind":"arxiv","id":"2608.05930","version":1},"attestation_state":"computed","paper":{"title":"Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Dimitris Rizopoulos, Loes Keijsers, Manon Hillegers, Nina van Gerwen, Sten Willemsen","submitted_at":"2026-08-06T11:59:30Z","abstract_excerpt":"The experience sampling method (ESM) is a longitudinal research design where participants report their thoughts, emotional states and behaviours multiple times a day. Our work is motivated by such data collected by the GrowIt! app, which was released to investigate daily emotions among adolescents during the COVID-19 pandemic. Current procedures to analyse ESM data face various challenges. While standard statistical techniques may not scale well to a high-dimensional setting, machine learning procedures can give biased results due to selection bias introduced by missingness. In our motivating "},"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":"2608.05930","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2026-08-06T11:59:30Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b37698a92af09956d4886a8c29add63781bc12b88b3cb401619f79048d9b307f","abstract_canon_sha256":"f88b342adfbdbbcc01cd6c74145cdd815dced912bc4bead9b60f89ac0c996555"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-08-07T00:53:25.679181Z","signature_b64":"xybrUE1hLbL5AyxhdW333CwgBn8WzYQFSUVSabWKd/bcG5EZ/86OZGqjSzmfetIdLjjN1vewcPE+rlt4i6BcAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c6df421cdbd9eecef4b9e5edc3195d31b056e39b2dd09bd758c1cef4c1c00d15","last_reissued_at":"2026-08-07T00:53:25.677621Z","signature_status":"signed_v1","first_computed_at":"2026-08-07T00:53:25.677621Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deep Generalised Mixed Models: a Novel Neural Network Structure for Analysing Hierarchical Data","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Dimitris Rizopoulos, Loes Keijsers, Manon Hillegers, Nina van Gerwen, Sten Willemsen","submitted_at":"2026-08-06T11:59:30Z","abstract_excerpt":"The experience sampling method (ESM) is a longitudinal research design where participants report their thoughts, emotional states and behaviours multiple times a day. Our work is motivated by such data collected by the GrowIt! app, which was released to investigate daily emotions among adolescents during the COVID-19 pandemic. Current procedures to analyse ESM data face various challenges. While standard statistical techniques may not scale well to a high-dimensional setting, machine learning procedures can give biased results due to selection bias introduced by missingness. In our motivating "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2608.05930","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/2608.05930/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":"2608.05930","created_at":"2026-08-07T00:53:25.679192+00:00"},{"alias_kind":"arxiv_version","alias_value":"2608.05930v1","created_at":"2026-08-07T00:53:25.679192+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2608.05930","created_at":"2026-08-07T00:53:25.679192+00:00"},{"alias_kind":"pith_short_12","alias_value":"Y3PUEHG33HXM","created_at":"2026-08-07T00:53:25.679192+00:00"},{"alias_kind":"pith_short_16","alias_value":"Y3PUEHG33HXM55FZ","created_at":"2026-08-07T00:53:25.679192+00:00"},{"alias_kind":"pith_short_8","alias_value":"Y3PUEHG3","created_at":"2026-08-07T00:53:25.679192+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/Y3PUEHG33HXM55FZ4XW4GGK5GG","json":"https://pith.science/pith/Y3PUEHG33HXM55FZ4XW4GGK5GG.json","graph_json":"https://pith.science/api/pith-number/Y3PUEHG33HXM55FZ4XW4GGK5GG/graph.json","events_json":"https://pith.science/api/pith-number/Y3PUEHG33HXM55FZ4XW4GGK5GG/events.json","paper":"https://pith.science/paper/Y3PUEHG3"},"agent_actions":{"view_html":"https://pith.science/pith/Y3PUEHG33HXM55FZ4XW4GGK5GG","download_json":"https://pith.science/pith/Y3PUEHG33HXM55FZ4XW4GGK5GG.json","view_paper":"https://pith.science/paper/Y3PUEHG3","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2608.05930&json=true","fetch_graph":"https://pith.science/api/pith-number/Y3PUEHG33HXM55FZ4XW4GGK5GG/graph.json","fetch_events":"https://pith.science/api/pith-number/Y3PUEHG33HXM55FZ4XW4GGK5GG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/Y3PUEHG33HXM55FZ4XW4GGK5GG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/Y3PUEHG33HXM55FZ4XW4GGK5GG/action/storage_attestation","attest_author":"https://pith.science/pith/Y3PUEHG33HXM55FZ4XW4GGK5GG/action/author_attestation","sign_citation":"https://pith.science/pith/Y3PUEHG33HXM55FZ4XW4GGK5GG/action/citation_signature","submit_replication":"https://pith.science/pith/Y3PUEHG33HXM55FZ4XW4GGK5GG/action/replication_record"}},"created_at":"2026-08-07T00:53:25.679192+00:00","updated_at":"2026-08-07T00:53:25.679192+00:00"}