{"id":"W2944250068","doi":"10.1080/23737484.2019.1605632","title":"Destructive cure rate models under proportional odds and associated likelihood inference","year":2019,"lang":"en","type":"article","venue":"Communications in Statistics Case Studies Data Analysis and Applications","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Natural Sciences and Engineering Research Council of Canada; Innovative Research Group Project of the National Natural Science Foundation of China","keywords":"Negative binomial distribution; Weibull distribution; Statistics; Poisson distribution; Odds; Mathematics; Inference; Poisson regression; Econometrics; Expectation–maximization algorithm; Logistic regression; Maximization; Maximum likelihood; Computer science; Artificial intelligence; Mathematical optimization; Medicine; Population","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0006746686,0.0002123569,0.0004690914,0.000228986,0.0005720656,0.00009683864,0.0004737892,0.00007883263,0.00005240358],"category_scores_gemma":[0.0007868502,0.0002084367,0.0000338623,0.001381822,0.0005655541,0.000262986,0.0009499927,0.000271273,0.00001318501],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009474641,"about_ca_system_score_gemma":0.00007931115,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000659453,"about_ca_topic_score_gemma":0.001510094,"domain_scores_codex":[0.9981269,0.0002048481,0.0007029399,0.0005566721,0.0001840531,0.000224599],"domain_scores_gemma":[0.9935171,0.003351696,0.0003376249,0.002145944,0.0005326477,0.0001150131],"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":[0.000002532238,0.0002180463,0.001279123,0.00003691282,0.0005332025,0.000002881557,0.0001533678,0.0001478112,0.00001018236,0.993012,0.0004788095,0.004125133],"study_design_scores_gemma":[0.0004002043,0.00001327032,0.01185791,0.00002532523,0.001126978,0.00002826784,0.002155797,0.3122999,0.000002168637,0.6714734,0.0003398264,0.0002769145],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005785617,0.0005499658,0.9800335,0.0008205546,0.000009470513,0.000785635,0.01142858,0.00005630528,0.0005303785],"genre_scores_gemma":[0.882932,0.002387515,0.107584,0.00008814919,0.000008950053,0.0005877982,0.006299607,0.00001563724,0.0000963934],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8771464,"threshold_uncertainty_score":0.8499803,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2252418149287024,"score_gpt":0.4690244187872457,"score_spread":0.2437826038585432,"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."}}