{"id":"W4221121685","doi":"10.1002/cjs.11696","title":"Subgroup analysis for functional partial linear regression model","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Eunice Kennedy Shriver National Institute of Child Health and Human Development; National Institutes of Health; China Postdoctoral Science Foundation; Education Department of Jiangxi Province; South University of Science and Technology of China; National Natural Science Foundation of China","keywords":"Functional principal component analysis; Mathematics; Covariate; Subgroup analysis; Functional data analysis; Regression analysis; Consistency (knowledge bases); Principal component analysis; Estimator; Scalar (mathematics); Additive model; Linear model; Population; Eigenvalues and eigenvectors; Statistics; Econometrics; Applied mathematics; Medicine; Discrete mathematics","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":[0.01653598,0.001527489,0.002437456,0.002073394,0.0008647771,0.001270695,0.002703604,0.001504462,0.003661312],"category_scores_gemma":[0.03754655,0.000726138,0.00289704,0.001511574,0.001803492,0.002507532,0.002356088,0.002913592,0.0004587823],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00121082,"about_ca_system_score_gemma":0.001931937,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00430192,"about_ca_topic_score_gemma":0.002957693,"domain_scores_codex":[0.9905342,0.007162705,0.0002536858,0.001032076,0.0007308523,0.0002866385],"domain_scores_gemma":[0.9836251,0.01146753,0.0009270253,0.001899484,0.0017726,0.0003083336],"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":[0.0005139307,0.0001939257,0.01398833,0.0003921912,0.000973461,0.0006933406,0.0007871788,0.4964806,0.002638023,0.2998355,0.00518031,0.1783231],"study_design_scores_gemma":[0.00002008027,0.00007933249,0.0005949329,0.00001897022,0.0000611951,0.00004675469,0.00004056736,0.9109501,0.0003231413,0.08685045,0.0009966397,0.00001788966],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008346279,0.000106585,0.9908386,0.0001711317,0.00001804425,0.00005943825,0.00005824761,0.0001174781,0.0002842163],"genre_scores_gemma":[0.5013148,0.0004815148,0.492212,0.0004215408,0.0001809494,0.0009881578,0.0009776854,0.0003102499,0.003113119],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01653598,"threshold_uncertainty_score":0.0874517,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1829746926477755,"score_gpt":0.3533131204362438,"score_spread":0.1703384277884682,"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."}}