{"id":"W3171092181","doi":"10.5539/ijsp.v10n4p62","title":"Marginalized Maximum Likelihood for Parameters Estimation of the Three Parameter Weibull Distribution","year":2021,"lang":"en","type":"article","venue":"International Journal of Statistics and Probability","topic":"Statistical Distribution Estimation and Applications","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Estimator; Weibull distribution; Mathematics; Statistics; Independent and identically distributed random variables; Order statistic; Statistic; Likelihood function; Monte Carlo method; Mean squared error; M-estimator; Maximum likelihood; Random variable","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":[0.0005440025,0.000102174,0.0002220237,0.00002344027,0.00007397393,0.00005718981,0.0001928541,0.00005264248,0.00007198538],"category_scores_gemma":[0.006593608,0.00007638562,0.0001115916,0.000099162,0.0001715977,0.00007900975,0.00005266561,0.0001192737,7.77432e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008861541,"about_ca_system_score_gemma":0.0001603188,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000648429,"about_ca_topic_score_gemma":0.00001513461,"domain_scores_codex":[0.9985483,0.00008017947,0.0007240897,0.0001338852,0.0004010932,0.0001124208],"domain_scores_gemma":[0.9955968,0.001850207,0.0005990566,0.0001654499,0.001719251,0.00006926613],"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":[0.0001431393,0.000333792,0.00111768,0.0001386394,0.0001404113,0.000002517652,0.00006345248,0.0001528145,0.0001314823,0.9446381,0.002919923,0.0502181],"study_design_scores_gemma":[0.0007472384,0.00005610415,0.01622625,0.00006485065,0.0001060566,0.00004360871,0.00001924286,0.02038259,0.001147397,0.9603967,0.0007350578,0.00007490099],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.06264541,0.00003106151,0.9308012,0.002000693,0.0002720698,0.0002547482,0.003960049,0.000005573475,0.00002921331],"genre_scores_gemma":[0.5930994,0.00001611721,0.4065905,0.00005896562,0.00002724226,0.00001977166,0.0001711278,0.000006449413,0.0000104452],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.530454,"threshold_uncertainty_score":0.7893642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0486563834881221,"score_gpt":0.3382466550812411,"score_spread":0.289590271593119,"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."}}