{"id":"W115106714","doi":"10.1007/0-387-24555-3_3","title":"Bayesian Functional Estimation of Hazard Rates for Randomly Right Censored Data Using Fourier Series Methods","year":2005,"lang":"en","type":"book-chapter","venue":"","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":false,"ca_institutions":"HEC Montréal; Université de Montréal; Group for Research in Decision Analysis","funders":"","keywords":"Estimator; Nonparametric statistics; Bayesian probability; Series (stratigraphy); Hazard ratio; Statistics; Parametric statistics; Fourier series; Data set; Estimation; Mathematics; Bayes estimator; Hazard; Computer science; Confidence interval; Engineering","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.001476696,0.0003697402,0.0009107092,0.000125489,0.0001163754,0.00004885563,0.0002833434,0.0003260463,0.004755495],"category_scores_gemma":[0.003350765,0.0002889281,0.0001560287,0.000030453,0.0001948983,0.0002193705,0.0001418548,0.0001932666,0.000005611162],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004385737,"about_ca_system_score_gemma":0.0001530103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000005656565,"about_ca_topic_score_gemma":0.00001269424,"domain_scores_codex":[0.9979945,0.0001219991,0.000880609,0.0004736169,0.0003106135,0.0002186283],"domain_scores_gemma":[0.9933468,0.004894585,0.0005185011,0.000813837,0.0003403637,0.00008592426],"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":[0.0002761876,0.0000195562,4.214036e-7,0.0002954648,0.0001770295,9.01694e-7,0.00001836579,0.00003875331,0.00007211572,0.9320596,0.004021735,0.06301983],"study_design_scores_gemma":[0.0007752656,0.00005356222,0.000002678553,0.0001764833,0.0004367032,0.0000117381,0.000005836623,0.1988877,0.0007919901,0.7665709,0.03200534,0.0002818445],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.000001655,0.0001017898,0.9191983,0.0001413041,0.0002668816,0.0006195197,0.0009684368,0.00004733136,0.0786548],"genre_scores_gemma":[0.00001034724,0.00002063516,0.8817198,0.00004056148,0.0002599619,0.00001483358,0.0003129408,0.00006567116,0.1175553],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1988489,"threshold_uncertainty_score":0.9999563,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2113486353972992,"score_gpt":0.4538355542376254,"score_spread":0.2424869188403263,"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."}}