{"id":"W2031212901","doi":"10.1002/cjs.10047","title":"Model‐based clustering of longitudinal data","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":146,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Natural Sciences and Engineering Research Council of Canada; Science Foundation Ireland","keywords":"Bayesian information criterion; Cluster analysis; Information Criteria; Model selection; Covariance; Convergence (economics); Computer science; Statistical model; Bayesian probability; Exponential family; Expectation–maximization algorithm; Mathematics; Data mining; Statistics; Maximum likelihood","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":true,"about_ca":true,"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":[0.01967021,0.001163657,0.002187808,0.005353251,0.001279971,0.002816747,0.003665449,0.001693164,0.003668268],"category_scores_gemma":[0.05271776,0.001086017,0.002910503,0.004080542,0.001508059,0.002790509,0.002640451,0.003046434,0.001308857],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002738387,"about_ca_system_score_gemma":0.002582241,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0158412,"about_ca_topic_score_gemma":0.01095846,"domain_scores_codex":[0.9897693,0.006297302,0.0004432167,0.001762938,0.001384281,0.0003429757],"domain_scores_gemma":[0.9809326,0.01127215,0.002008554,0.003322166,0.002107686,0.0003569667],"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":[0.0003198886,0.0001271288,0.02216068,0.000382734,0.001457438,0.0002630906,0.0006812452,0.5629646,0.002471898,0.2101635,0.0146376,0.1843701],"study_design_scores_gemma":[0.00002833638,0.00002915663,0.003225267,0.00006513798,0.00008720091,0.00009773539,0.00005738317,0.8745636,0.0005168287,0.1166314,0.004625966,0.00007192652],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.006200692,0.0003697991,0.9919627,0.0002666478,0.00004044244,0.00007487569,0.0004506292,0.0002606638,0.0003735217],"genre_scores_gemma":[0.3303365,0.001523194,0.6544172,0.0003338295,0.0002367204,0.001283112,0.006927801,0.0004977735,0.004443994],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01967021,"threshold_uncertainty_score":0.1040272,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09939433401187582,"score_gpt":0.2985575540033773,"score_spread":0.1991632199915015,"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."}}