{"id":"W3167721112","doi":"10.1002/cjs.11619","title":"Hazard regression with noncompactly supported bases","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Estimator; Generalization; Mathematics; Nonparametric statistics; Hazard; Regression; Nonparametric regression; Statistics; Function (biology); Hazard ratio; Applied mathematics; Basis (linear algebra); Regression analysis; Upper and lower bounds; Econometrics; Confidence interval; Mathematical analysis","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":[0.006743971,0.0008289666,0.001304761,0.0008188148,0.0002874528,0.001682268,0.001363201,0.0009460124,0.001515378],"category_scores_gemma":[0.02515461,0.0005114463,0.00069092,0.001119639,0.00179063,0.002201995,0.001946102,0.002840017,0.0004759994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004548454,"about_ca_system_score_gemma":0.0006950572,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00126087,"about_ca_topic_score_gemma":0.0007592063,"domain_scores_codex":[0.9976406,0.001232661,0.00009653301,0.0003086335,0.0005726287,0.000148968],"domain_scores_gemma":[0.9822111,0.01253481,0.001572909,0.002142764,0.001224088,0.0003144547],"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":[0.0004462681,0.0001383616,0.003215302,0.0001809092,0.0001084184,0.0003724944,0.0002351783,0.3231246,0.005580839,0.5877253,0.0007356959,0.07813651],"study_design_scores_gemma":[0.00004136884,0.0001214469,0.0006065358,0.0000265987,0.00001437284,0.00008320718,0.00003420216,0.8752324,0.00140242,0.121548,0.000860925,0.00002855398],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04549823,0.0003409165,0.9529214,0.000167815,0.00003045847,0.00001760491,0.0000479918,0.00007993955,0.0008957298],"genre_scores_gemma":[0.7244974,0.0008621655,0.2683813,0.0001887701,0.0002134829,0.000156213,0.0004114409,0.00008299181,0.005206153],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006743971,"threshold_uncertainty_score":0.03566593,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09708599890473622,"score_gpt":0.3396319397923857,"score_spread":0.2425459408876495,"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."}}