{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007807282,0.000108194,0.0003272286,0.00030965,0.0003759555,0.00002895572,0.0001713418,0.00003546442,0.001160301],"category_scores_gemma":[0.002344575,0.00009620741,0.0001334427,0.0003004421,0.000065988,0.000037666,0.00001735881,0.000271671,0.000001078007],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001799964,"about_ca_system_score_gemma":0.001079681,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002106813,"about_ca_topic_score_gemma":0.001179109,"domain_scores_codex":[0.9986849,0.0001096949,0.0005104665,0.0001177576,0.000314586,0.0002625808],"domain_scores_gemma":[0.9975392,0.001134443,0.0003418864,0.0001345368,0.0003981885,0.0004517589],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0001281275,0.00005472356,0.00237384,0.00004856232,0.0004239581,0.0001066069,0.0004184604,0.0280441,0.00004194079,0.8869883,0.07402562,0.007345778],"study_design_scores_gemma":[0.0004494584,0.0002801893,0.0008515624,0.00001107226,0.0006914021,0.00003245095,0.0001758752,0.4747045,0.00003609019,0.5178422,0.004767015,0.0001582606],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.005433717,0.0000448154,0.990343,0.0001742616,0.0003853145,0.00008463069,0.003410504,0.000003690107,0.0001200454],"genre_scores_gemma":[0.2674344,0.000002344239,0.7318915,0.0001432932,0.0001467337,0.00001099416,0.00004015187,0.00001686511,0.0003137066],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4466604,"threshold_uncertainty_score":0.9997528,"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."}}