{"id":"W3137829607","doi":"10.1007/s42081-021-00115-1","title":"Likelihood analysis and stochastic EM algorithm for left truncated right censored data and associated model selection from the Lehmann family of life distributions","year":2021,"lang":"en","type":"article","venue":"Japanese Journal of Statistics and Data Science","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":23,"is_retracted":false,"has_abstract":false,"ca_institutions":"McMaster University","funders":"","keywords":"Mathematics; Model selection; Weibull distribution; Expectation–maximization algorithm; Context (archaeology); Monte Carlo method; Parametric model; Parametric statistics; Inference; Statistics; Applied mathematics; Algorithm; Maximum likelihood; 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":[],"consensus_categories":[],"category_scores_codex":[0.0009970096,0.00009525817,0.0002748364,0.00006688741,0.0004461756,0.0001602,0.0003954349,0.00003127178,0.00001335751],"category_scores_gemma":[0.007212885,0.00006840234,0.00001718821,0.0006580667,0.0003690915,0.00039426,0.0002395208,0.0001016721,1.93043e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002317461,"about_ca_system_score_gemma":0.0002631235,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003132467,"about_ca_topic_score_gemma":0.0001243071,"domain_scores_codex":[0.9986247,0.00004881615,0.0005173402,0.000292181,0.0003612309,0.0001556651],"domain_scores_gemma":[0.996323,0.00170841,0.000398406,0.0003964965,0.0009891782,0.0001844737],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000298941,0.003459707,0.008874569,0.000242846,0.003634988,0.00002079038,0.007215521,0.002832253,0.04244613,0.7713745,0.02635972,0.13324],"study_design_scores_gemma":[0.0004442126,0.0000274639,0.04985066,0.00001337319,0.0006005901,0.0000102217,0.000663485,0.9008768,0.0000374647,0.04738426,0.00001789269,0.00007364506],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.08640765,0.00005811777,0.8555918,0.000229829,0.00001582633,0.00009707174,0.0575919,0.000005291743,0.000002508756],"genre_scores_gemma":[0.6495727,0.00005195838,0.3480528,0.00003577995,0.00001233861,0.000001739468,0.002265472,0.000003520696,0.000003636595],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8980445,"threshold_uncertainty_score":0.8635018,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06169436170705538,"score_gpt":0.3539762867601199,"score_spread":0.2922819250530646,"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."}}