{"id":"W2158741947","doi":"10.1080/03610920600692789","title":"Inference for Log-Gamma Distribution Based on Progressively Type-II Censored Data","year":2006,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":33,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Censoring (clinical trials); Mathematics; Estimator; Statistics; Maximum likelihood; Expectation–maximization algorithm; Maximum likelihood sequence estimation; M-estimator; Restricted maximum likelihood; Inference; Gamma distribution; Estimation theory; Applied mathematics; Computer science; Artificial intelligence","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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.002738414,0.0001601372,0.0002330777,0.00005850693,0.0003906548,0.00005516854,0.000453183,0.00009577844,0.00008907816],"category_scores_gemma":[0.01150106,0.0001534839,0.00001893525,0.0002446391,0.0003086071,0.00009009201,0.0001639672,0.0001802948,0.000004938148],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005804443,"about_ca_system_score_gemma":0.00007276924,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0000102675,"about_ca_topic_score_gemma":0.00001188183,"domain_scores_codex":[0.9977383,0.001134281,0.0004950166,0.0003066225,0.0001277555,0.000197996],"domain_scores_gemma":[0.9876685,0.01062083,0.0002435135,0.001120516,0.0002829084,0.00006374646],"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":[0.0001313689,0.0002713687,0.00007904721,0.0000633942,0.000005571519,2.463526e-7,0.00003932968,0.00004288206,0.00005536413,0.9572796,0.004314246,0.03771759],"study_design_scores_gemma":[0.000657875,0.00006988464,0.00580915,0.00008351148,0.00004492406,9.505112e-7,0.00004561546,0.1185826,0.0002100473,0.8656301,0.008696172,0.0001690504],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003470448,0.0001023815,0.9917755,0.0004059711,0.00004684454,0.0006394571,0.005850607,0.00006959977,0.0007626199],"genre_scores_gemma":[0.2899369,0.00002407457,0.7017421,0.00007890267,0.00001618565,0.0002043848,0.00782489,0.00001420879,0.0001584002],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2900334,"threshold_uncertainty_score":0.9968255,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1547408017370585,"score_gpt":0.5124889106673429,"score_spread":0.3577481089302844,"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."}}