{"id":"W2771768318","doi":"10.1080/00949655.2017.1409748","title":"Estimation of partially linear single-index additive hazards model with current status data","year":2017,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Estimator; Nonparametric statistics; Statistics; Semiparametric regression; Monotonic function; Semiparametric model; Applied mathematics; Parametric statistics; Single-index model; Proportional hazards model; Mathematical optimization","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.006214289,0.0006809569,0.00168932,0.0008796825,0.0003256137,0.001379805,0.00330146,0.0009600386,0.00165331],"category_scores_gemma":[0.01552161,0.0006665124,0.001438554,0.001421027,0.0008454061,0.001836148,0.001528119,0.001778959,0.0003508551],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005311113,"about_ca_system_score_gemma":0.00183387,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004229757,"about_ca_topic_score_gemma":0.003244253,"domain_scores_codex":[0.9974877,0.00126543,0.0001331706,0.0005371476,0.0004093347,0.0001673041],"domain_scores_gemma":[0.9914311,0.006110566,0.000942726,0.0008347543,0.0005290683,0.0001516851],"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":[0.0004623725,0.0001558741,0.0310132,0.0003936373,0.0003785188,0.0006969626,0.0003418066,0.7982377,0.001236563,0.06804835,0.001817198,0.09721794],"study_design_scores_gemma":[0.0000304925,0.00009624442,0.002402315,0.00001898472,0.00005483773,0.000117118,0.00003188372,0.9710554,0.0003354752,0.0248834,0.000950327,0.00002354202],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.04610321,0.0004136191,0.9515519,0.0004763981,0.00003907924,0.00009281891,0.0006266959,0.0001839996,0.0005123745],"genre_scores_gemma":[0.7716921,0.001057273,0.2191544,0.0002690474,0.0001890812,0.0006475933,0.002713654,0.00006215917,0.00421472],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.006214289,"threshold_uncertainty_score":0.03286469,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1885143484954265,"score_gpt":0.4629313583657982,"score_spread":0.2744170098703717,"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."}}