{"id":"W4303650241","doi":"10.1007/s00180-022-01286-5","title":"Pretest and shrinkage estimators for log-normal means","year":2022,"lang":"en","type":"article","venue":"Computational Statistics","topic":"Bayesian Methods and Mixture Models","field":"Computer Science","cited_by":10,"is_retracted":false,"has_abstract":false,"ca_institutions":"Brock University","funders":"","keywords":"Estimator; Pooling; Mathematics; Homogeneity (statistics); Shrinkage; Statistics; Population; Applied mathematics; Shrinkage estimator; Asymptotic analysis; Econometrics; Computer science; Artificial intelligence; Efficient estimator; Medicine","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":[0.0003060948,0.0001003494,0.0001192686,0.00005779081,0.000406466,0.00008759823,0.0002909013,0.00001807631,0.0000213097],"category_scores_gemma":[0.00007065921,0.0001090037,0.00002379003,0.0001256973,0.00004516944,0.0001098736,0.0002580032,0.0001055145,0.000001801213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003420323,"about_ca_system_score_gemma":0.00009777107,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000003903168,"about_ca_topic_score_gemma":9.62998e-7,"domain_scores_codex":[0.9990511,0.00007806369,0.000179256,0.0002687548,0.0002424229,0.0001804006],"domain_scores_gemma":[0.9989218,0.0007080499,0.00007408944,0.0001273529,0.00008468264,0.00008407576],"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":[0.000004836246,0.00002641348,0.0001161778,0.00001843638,0.00001055203,0.000008710363,0.0002874046,0.0232576,0.000005658344,0.9108442,0.004991279,0.06042871],"study_design_scores_gemma":[0.0001983668,0.0000848377,0.001222586,0.000001382244,0.000005171347,0.0000281955,0.000004010166,0.5928926,0.000003910262,0.402586,0.002882798,0.00009008493],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002961682,0.00008889105,0.9981213,0.000247273,0.0002641023,0.0002036636,0.0004468584,0.00005977901,0.0002719168],"genre_scores_gemma":[0.06244526,0.000001552649,0.9368053,0.0004448318,0.00003273796,0.00005549079,0.0000806123,0.000009756332,0.0001244649],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.569635,"threshold_uncertainty_score":0.4445042,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01716372547539307,"score_gpt":0.2767689234488893,"score_spread":0.2596051979734962,"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."}}