{"id":"W4304780016","doi":"10.1002/cjs.11733","title":"Minorize–maximize algorithm for the generalized odds rate model for clustered current status data","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Key Research and Development Program of China; National Institutes of Health; National Natural Science Foundation of China","keywords":"Nonparametric statistics; Cluster analysis; Parametric statistics; Expectation–maximization algorithm; Algorithm; Random effects model; Computer science; Mixture model; Mathematics; Data mining; Statistics; Mathematical optimization; Meta-analysis; Maximum likelihood; Medicine","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001874409,0.0001901012,0.0004390049,0.00012251,0.000629714,0.0001125578,0.0008960754,0.0000355592,0.000240186],"category_scores_gemma":[0.004910907,0.0001477141,0.0000884807,0.0001223673,0.000119807,0.00007349746,0.0001013379,0.0003507297,6.551178e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002442982,"about_ca_system_score_gemma":0.002523699,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004889496,"about_ca_topic_score_gemma":0.002052394,"domain_scores_codex":[0.9979536,0.0002094139,0.0007666107,0.000217439,0.0002769047,0.0005760342],"domain_scores_gemma":[0.9929729,0.004863782,0.0005300168,0.0005117297,0.0005591435,0.0005624113],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001020204,0.00004628478,0.00001309188,0.0001129282,0.0001571026,0.00002272164,0.0006066604,0.001297181,0.000008405899,0.2639365,0.2716435,0.4620537],"study_design_scores_gemma":[0.001102135,0.0001453812,0.0000190261,0.00001257991,0.0002250693,0.0000181696,0.0001503058,0.640268,0.000002713949,0.3078468,0.05007579,0.0001341007],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.00007566733,0.0008292216,0.9304214,0.0003593421,0.001484777,0.0005785802,0.06622738,0.000004589248,0.00001900299],"genre_scores_gemma":[0.001172355,0.0001109524,0.9974958,0.0002899176,0.0002824966,0.00007457354,0.0002223408,0.00004654948,0.0003050065],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.6389708,"threshold_uncertainty_score":0.6023608,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.3841466383193652,"score_gpt":0.3921805179367636,"score_spread":0.008033879617398376,"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."}}