{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006968,0.0002028908,0.0003756447,0.0004158184,0.0001382827,0.0004628413,0.0003814886,0.0002328495,0.00003013251],"category_scores_gemma":[0.0006415058,0.0001929239,0.00003810046,0.0002243786,0.00003865395,0.000135024,0.0001116166,0.001130585,5.911347e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002627721,"about_ca_system_score_gemma":0.002335906,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002077413,"about_ca_topic_score_gemma":0.01077744,"domain_scores_codex":[0.9984822,0.000222565,0.0004878307,0.0003055601,0.0002078915,0.0002940102],"domain_scores_gemma":[0.9981583,0.0003175536,0.0003599289,0.0002505107,0.0004141445,0.0004995115],"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":[0.00005944853,0.00009098234,0.001742245,0.0008305846,0.0002699706,0.009473633,0.007663454,0.1838529,0.0001911771,0.205525,0.01499512,0.5753056],"study_design_scores_gemma":[0.0006301075,0.0003422841,0.001290285,0.002359032,0.00007858314,0.001013361,0.0001046656,0.7751309,0.00004497953,0.2112865,0.007117923,0.0006013133],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0005635041,0.0008103373,0.9962143,0.000225948,0.001701919,0.00007813377,0.00003794815,0.000006965296,0.0003609294],"genre_scores_gemma":[0.09462988,0.00009048356,0.9045475,0.0003773395,0.0001604445,0.000001359881,0.000005113114,0.00001774276,0.0001701727],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.5912781,"threshold_uncertainty_score":0.7867208,"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."}}