{"id":"W4362621355","doi":"10.1080/00949655.2023.2195657","title":"Robust hypothesis testing in functional linear models","year":2023,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Type I and type II errors; Mathematics; Covariate; Statistical hypothesis testing; Linear model; Functional data analysis; Generalized linear model; Scalar (mathematics); Linear regression; Variance (accounting); Statistics; Econometrics; Sequential analysis; Robustness (evolution); Statistical power; Computer science","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.0008237294,0.00008123335,0.0002150441,0.0002093081,0.00005572251,0.00003064429,0.00003169418,0.00004753227,0.00003558207],"category_scores_gemma":[0.008698167,0.00006921202,0.00002160094,0.000342025,0.00003926709,0.0001265305,0.00001644168,0.0001567318,0.000005143921],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002966178,"about_ca_system_score_gemma":0.0000387555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004320083,"about_ca_topic_score_gemma":0.000001441069,"domain_scores_codex":[0.9987761,0.0001431774,0.0005661965,0.0001030852,0.0002910157,0.0001204495],"domain_scores_gemma":[0.9837222,0.01566926,0.0001970579,0.00003309528,0.0003002625,0.00007812097],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00004863166,0.00003648538,0.0005884309,0.000039756,0.000007508219,0.00001746969,0.0001134869,0.8822525,0.00002628845,0.05773097,0.0001263849,0.05901207],"study_design_scores_gemma":[0.0002588641,0.00005720495,0.02429893,0.00002998732,0.000009104301,0.000005176511,0.00003574152,0.5290528,0.000001448305,0.4462089,0.000002498115,0.00003932644],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.09690278,0.000005445557,0.9026211,0.00007931613,0.00006993941,0.00006065053,0.00001237934,0.00001937527,0.0002290022],"genre_scores_gemma":[0.5370111,0.000002338815,0.4629041,0.00002238483,0.000044499,6.108574e-7,0.000001756477,0.000005953867,0.000007289962],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.4401083,"threshold_uncertainty_score":0.999652,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4201984437062276,"score_gpt":0.4089752939161696,"score_spread":0.01122314979005801,"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."}}