{"id":"W2015638363","doi":"10.1080/03610920902940191","title":"Semiparametric Estimation for Two-Sample Location-Scale Models under Type I Censorship","year":2010,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph; University of Calgary","funders":"Natural Sciences and Engineering Research Council of Canada; National Natural Science Foundation of China","keywords":"Censoring (clinical trials); Estimator; Inference; Monte Carlo method; Censorship; Sample (material); Scale (ratio); Statistical inference; Semiparametric model; Statistics; Mathematics; Range (aeronautics); Econometrics; Computer science; Artificial intelligence; Engineering; Geography; Law","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":[0.01966757,0.0007403434,0.001945714,0.001485981,0.0005014851,0.001827378,0.002816237,0.00137872,0.002496316],"category_scores_gemma":[0.1052323,0.0006153695,0.001437317,0.001762082,0.002455709,0.002912556,0.003141981,0.002200062,0.0003745858],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001017602,"about_ca_system_score_gemma":0.001056818,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001159878,"about_ca_topic_score_gemma":0.001128227,"domain_scores_codex":[0.9848607,0.011154,0.0004700767,0.001273359,0.001904635,0.0003371865],"domain_scores_gemma":[0.9185909,0.06674096,0.004362746,0.007649745,0.002233918,0.0004217513],"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":[0.0002987662,0.000190919,0.01161639,0.0005891835,0.0007602661,0.0003816556,0.0007943595,0.3594471,0.003323294,0.4430424,0.001791901,0.1777637],"study_design_scores_gemma":[0.00003573329,0.00008902667,0.002192044,0.0000391262,0.00006868697,0.0001400173,0.00008640397,0.8317944,0.001398987,0.1627601,0.001354482,0.00004091883],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.008019257,0.00009558552,0.9913858,0.00006652307,0.00001097533,0.00001659012,0.00002958806,0.00006626657,0.0003093916],"genre_scores_gemma":[0.5887264,0.0004080892,0.4081047,0.0001876173,0.0001079349,0.0003300698,0.0003398159,0.0001099281,0.001685438],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01966757,"threshold_uncertainty_score":0.1040133,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1630628407036584,"score_gpt":0.4991408431171003,"score_spread":0.3360780024134419,"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."}}