{"id":"W4399721808","doi":"10.1002/sim.10151","title":"A sparse factor model for clustering high‐dimensional longitudinal data","year":2024,"lang":"en","type":"article","venue":"Statistics in Medicine","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Queen's University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Cluster analysis; Computer science; Categorical variable; Dirichlet process; Curse of dimensionality; Gibbs sampling; Clustering high-dimensional data; Data mining; Bayesian probability; Artificial intelligence; Machine learning","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008476835,0.001065084,0.002078234,0.002207746,0.0009447585,0.001698214,0.003295593,0.002470934,0.002315095],"category_scores_gemma":[0.02153927,0.001013793,0.001957164,0.003021092,0.002476921,0.003205418,0.001805406,0.002858012,0.001058326],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001542209,"about_ca_system_score_gemma":0.001628786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009736656,"about_ca_topic_score_gemma":0.008493644,"domain_scores_codex":[0.9962698,0.002083784,0.0001624611,0.0007280715,0.0005441214,0.0002116467],"domain_scores_gemma":[0.9915096,0.005931174,0.0007293824,0.0007829849,0.0008559878,0.000190882],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001957835,0.00007386976,0.003186099,0.0001992075,0.0001749087,0.0001339334,0.0004261999,0.6441863,0.001838201,0.2878006,0.003339921,0.05844494],"study_design_scores_gemma":[0.00001602446,0.00002306561,0.0003391443,0.00001603052,0.00001841315,0.00004545682,0.00002128914,0.9291872,0.0001739105,0.06896033,0.001175366,0.00002370237],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003602546,0.0001838627,0.9955724,0.0001802764,0.00002152222,0.00003104559,0.0001115266,0.0000969406,0.0001997003],"genre_scores_gemma":[0.3366266,0.001789065,0.6521302,0.0004127938,0.0003291968,0.0008566469,0.002115283,0.0002047304,0.005535545],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.009736656,"threshold_uncertainty_score":0.04483032,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1298754081797527,"score_gpt":0.39157382235535,"score_spread":0.2616984141755973,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}