{"id":"W2074119503","doi":"10.1080/00949655.2012.696117","title":"Likelihood estimation for a general class of inverse exponentiated distributions based on complete and progressively censored data","year":2012,"lang":"en","type":"article","venue":"Journal of Statistical Computation and Simulation","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":63,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"","keywords":"Mathematics; Censoring (clinical trials); Statistics; Maximum likelihood; Applied mathematics; Class (philosophy); Exponential family","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008070343,0.0007260764,0.0008910486,0.001390989,0.000350913,0.001568696,0.001685651,0.001094543,0.002207977],"category_scores_gemma":[0.03818754,0.0005076211,0.0008688055,0.0009013055,0.001979622,0.003490405,0.002014619,0.001763682,0.0002944533],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00106622,"about_ca_system_score_gemma":0.0008869522,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0008579555,"about_ca_topic_score_gemma":0.0007000177,"domain_scores_codex":[0.9981589,0.0007721427,0.0001007229,0.0003890798,0.0004395904,0.0001397424],"domain_scores_gemma":[0.9803591,0.01423206,0.002093726,0.001694288,0.001323131,0.000297701],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003454666,0.000132626,0.03077642,0.0003546477,0.0002411617,0.0008036063,0.0005542772,0.4473889,0.007683089,0.4284532,0.001517008,0.08174954],"study_design_scores_gemma":[0.00004406358,0.00006224672,0.006431805,0.00005751218,0.00002365112,0.0003025938,0.0000943739,0.8158212,0.00229804,0.1736014,0.001213694,0.0000494711],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.08050843,0.0002465487,0.9177068,0.0002345485,0.00001394382,0.00004514249,0.0001571913,0.00007573192,0.00101152],"genre_scores_gemma":[0.8834537,0.0005634893,0.1116548,0.0001284376,0.00008528968,0.000160347,0.0006425065,0.00004876001,0.003262612],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.008070343,"threshold_uncertainty_score":0.04268062,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1247503735309609,"score_gpt":0.4121782381850584,"score_spread":0.2874278646540975,"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."}}