{"id":"W4387095339","doi":"10.1007/s11634-023-00558-2","title":"Parsimony and parameter estimation for mixtures of multivariate leptokurtic-normal distributions","year":2023,"lang":"en","type":"article","venue":"Advances in Data Analysis and Classification","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Waterloo","funders":"NextGenerationEU; Ministero dell'Università e della Ricerca; Natural Sciences and Engineering Research Council of Canada; Università Cattolica del Sacro Cuore","keywords":"Kurtosis; Multivariate statistics; Statistics; Mathematics; Estimation; Multivariate normal distribution; Multivariate analysis; Estimation theory; Econometrics; Economics","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005830365,0.0000734142,0.000178449,0.0001873343,0.00007225032,0.00005477617,0.0003028464,0.00004080362,8.233116e-7],"category_scores_gemma":[0.0002482008,0.00006288299,0.00003048859,0.0009470413,0.00005741126,0.0009616387,0.0001394409,0.0000418308,3.955403e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000007138583,"about_ca_system_score_gemma":0.00001059931,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001369277,"about_ca_topic_score_gemma":0.00006461652,"domain_scores_codex":[0.9990904,0.00006982466,0.000256997,0.0003736832,0.00008943611,0.0001196124],"domain_scores_gemma":[0.9988782,0.0003473701,0.0001362484,0.0005615412,0.00004320312,0.00003341092],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001226554,0.000044695,0.004828525,0.00004440594,0.00008932892,3.080383e-7,0.0001818796,0.0004834658,0.002363143,0.1975549,0.00008864588,0.7943084],"study_design_scores_gemma":[0.0001432242,0.00001255589,0.0627138,0.000007966669,0.0001165544,2.760513e-7,0.00001296923,0.8876049,0.0004675048,0.04809254,0.0007584865,0.00006923314],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.006040089,0.0004796066,0.9926611,0.0004841903,0.00003131881,0.000118717,0.0001412753,0.0000224621,0.00002126781],"genre_scores_gemma":[0.5513983,0.0006426534,0.4473392,0.00001096273,0.000006456043,0.00002451595,0.0005630404,0.000001979357,0.00001290068],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8871214,"threshold_uncertainty_score":0.2564294,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04852271423534097,"score_gpt":0.3659672425237264,"score_spread":0.3174445282883854,"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."}}