{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01142737,0.001694081,0.002127615,0.003375157,0.001438737,0.002983087,0.00564709,0.002699838,0.002763445],"category_scores_gemma":[0.03086101,0.001259328,0.002732315,0.00413938,0.002326692,0.004329479,0.004490479,0.003737168,0.00136453],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002010753,"about_ca_system_score_gemma":0.002221905,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00576658,"about_ca_topic_score_gemma":0.007452534,"domain_scores_codex":[0.9939877,0.003401988,0.0002435947,0.001172005,0.0009423656,0.0002523311],"domain_scores_gemma":[0.9866601,0.007305355,0.001303583,0.002534853,0.001839122,0.0003569718],"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":[0.000173104,0.0001081843,0.005872893,0.0001988467,0.0003811962,0.000195272,0.0005197107,0.6567188,0.002146316,0.2295841,0.004768451,0.09933314],"study_design_scores_gemma":[0.00001449868,0.00001338645,0.000417745,0.00002164187,0.00001595528,0.00005319517,0.00004383279,0.8652251,0.0004746314,0.1318963,0.001792972,0.00003069559],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003237548,0.00007383707,0.9960998,0.0001075236,0.00001258551,0.00002957647,0.0001097457,0.000160565,0.0001688129],"genre_scores_gemma":[0.1507766,0.000375006,0.8432097,0.0003348631,0.0001222455,0.0006743377,0.001977903,0.0005179726,0.002011282],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01142737,"threshold_uncertainty_score":0.06043446,"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."}}