{"id":"W2955711579","doi":"10.1002/cjs.11668","title":"Simultaneous variable selection, clustering, and smoothing in function‐on‐scalar regression","year":2021,"lang":"en","type":"preprint","venue":"Canadian Journal of Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Heart, Lung, and Blood Institute","keywords":"Multicollinearity; Scalar (mathematics); Smoothing; Cluster analysis; Dimensionality reduction; Regression analysis; Regression; Econometrics; Dimension (graph theory); Feature selection; Statistics; Mathematics; Computer science; Covariate; Data mining; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.01620175,0.001461042,0.002539895,0.002923946,0.001555676,0.001978052,0.002840391,0.00230589,0.001658996],"category_scores_gemma":[0.03958282,0.001482074,0.002866915,0.003532075,0.002613365,0.002283255,0.004111821,0.003143889,0.0005650161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00132731,"about_ca_system_score_gemma":0.002714383,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01057397,"about_ca_topic_score_gemma":0.009518447,"domain_scores_codex":[0.9907594,0.006844866,0.0002506825,0.001184638,0.0006684044,0.0002921538],"domain_scores_gemma":[0.9704087,0.02250171,0.001623351,0.003613927,0.001419446,0.0004328137],"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":[0.0003422401,0.0001825011,0.007024968,0.0001960298,0.0005329769,0.000152687,0.0007137252,0.6612741,0.002815716,0.1296131,0.001824147,0.1953278],"study_design_scores_gemma":[0.00003642922,0.00006307481,0.001087013,0.00002200047,0.00004111271,0.00003276459,0.00002496224,0.9441124,0.0007937506,0.0526209,0.001124831,0.00004067254],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01099355,0.0001454244,0.9882555,0.0001420235,0.000019958,0.00003177491,0.000035095,0.000221241,0.0001554222],"genre_scores_gemma":[0.2553113,0.0005699295,0.7409789,0.0001737317,0.0001696905,0.0003189341,0.0004417998,0.0002729365,0.001762766],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01620175,"threshold_uncertainty_score":0.08568406,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01505052437371114,"score_gpt":0.2446080087800594,"score_spread":0.2295574844063483,"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."}}