{"id":"W4417090136","doi":"10.48550/arxiv.2504.12683","title":"Cluster weighted models with multivariate skewed distributions for functional data","year":2025,"lang":"en","type":"preprint","venue":"ArXiv.org","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Multivariate statistics; Cluster analysis; Functional data analysis; Functional principal component analysis; Mixture model; Expectation–maximization algorithm; Cluster (spacecraft); Multivariate normal distribution; Gaussian","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0006834556,0.0004286323,0.0004796335,0.0001374559,0.0003074423,0.00019644,0.002512927,0.0003610922,0.00001021509],"category_scores_gemma":[0.00007973731,0.0003442876,0.0001523469,0.0002955254,0.00007521655,0.0005874746,0.003974632,0.0006434114,0.00001032851],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009415718,"about_ca_system_score_gemma":0.0006716266,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00008875842,"about_ca_topic_score_gemma":0.00003147411,"domain_scores_codex":[0.9970233,0.0001943784,0.0004320026,0.00159638,0.0002862308,0.0004677293],"domain_scores_gemma":[0.9956622,0.0003634244,0.0002390374,0.003210506,0.0003741112,0.0001507912],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0004301496,0.0007854782,0.002825442,0.0006756076,0.001377074,0.00002412427,0.0006631612,0.006495696,0.0002323358,0.9056381,0.03816278,0.04269005],"study_design_scores_gemma":[0.001017674,0.00003765225,0.002909316,0.0001948647,0.0001395887,0.000006153457,0.000002214195,0.8407537,0.0001736281,0.1486921,0.005580438,0.000492672],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001102501,0.0001588295,0.9903296,0.003113557,0.001343537,0.0009947984,0.001833574,0.0002606881,0.0008629455],"genre_scores_gemma":[0.06217332,0.00002423094,0.9319496,0.0006664582,0.0003357749,0.0003301108,0.002316759,0.00002459436,0.002179128],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.834258,"threshold_uncertainty_score":0.9999009,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1255811842507812,"score_gpt":0.3258934414331883,"score_spread":0.2003122571824072,"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."}}