{"id":"W2163009348","doi":"10.1007/s10463-008-0170-8","title":"Proportional hazards regression under progressive Type-II censoring","year":2008,"lang":"en","type":"article","venue":"Annals of the Institute of Statistical Mathematics","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Censoring (clinical trials); Mathematics; Estimator; Statistics; Proportional hazards model; Monte Carlo method; Econometrics; Martingale (probability theory)","routes":{"ca_aff":true,"ca_fund":false,"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":[],"consensus_categories":[],"category_scores_codex":[0.0002426288,0.0001837347,0.0003964569,0.00005097947,0.000332994,0.000008078466,0.0003193379,0.00008969488,0.0002992051],"category_scores_gemma":[0.003395948,0.0001197236,0.0001098865,0.0003412292,0.0009778251,0.0001077149,0.0001603933,0.0001603895,0.00001652994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002134041,"about_ca_system_score_gemma":0.0002116798,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005710254,"about_ca_topic_score_gemma":0.000001089034,"domain_scores_codex":[0.9980161,0.00004009111,0.000845736,0.0001865618,0.0006921481,0.0002193612],"domain_scores_gemma":[0.9976224,0.0004736819,0.0005950087,0.0004583769,0.0007388449,0.0001116325],"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":[0.00001939812,0.0006405204,0.00003808498,0.0002913643,0.00004738104,0.000004326565,0.0001711382,0.00006503474,0.0003336495,0.9882668,0.009611306,0.0005109942],"study_design_scores_gemma":[0.0003161411,0.0001090573,0.004074771,0.000472117,0.00007339629,0.00007401274,0.00007889543,0.003714001,0.01026218,0.9797583,0.000864576,0.0002026029],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2118008,0.00006757399,0.7784013,0.002678976,0.0003136748,0.0008326651,0.0009247319,0.00008498382,0.004895295],"genre_scores_gemma":[0.8087953,0.00002130417,0.1906657,0.00008088782,0.0000338666,0.0000253797,0.00004065518,0.00001756465,0.0003193452],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5969945,"threshold_uncertainty_score":0.4882185,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.208747829864109,"score_gpt":0.4269136210230616,"score_spread":0.2181657911589526,"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."}}