{"id":"W1997934667","doi":"10.1111/j.0006-341x.2005.030833.x","title":"Bias‐Corrected Maximum Likelihood Estimator of the Negative Binomial Dispersion Parameter","year":2005,"lang":"en","type":"article","venue":"Biometrics","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":122,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Statistics; Estimator; Negative binomial distribution; Restricted maximum likelihood; Quasi-likelihood; Bias of an estimator; Maximum likelihood; Minimum-variance unbiased estimator; Poisson distribution","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0006798297,0.0001587684,0.0002453805,0.0006928359,0.0001092206,0.00003494575,0.0003123102,0.0001417139,0.0000593019],"category_scores_gemma":[0.009915211,0.0001070646,0.0001472535,0.003249811,0.00009935313,0.00009906333,0.0001134504,0.0001411982,0.00002650674],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00009735299,"about_ca_system_score_gemma":0.00005420262,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00006420394,"about_ca_topic_score_gemma":0.000008140232,"domain_scores_codex":[0.9986742,0.0001213708,0.0004106979,0.0001954371,0.0003766155,0.000221655],"domain_scores_gemma":[0.9967009,0.002278084,0.000307954,0.000434788,0.0002110037,0.00006720794],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0002165288,0.001976212,0.05287379,0.0003871524,0.000236464,0.000002864613,0.002512862,0.00004927542,0.009580364,0.00375367,0.08264417,0.8457667],"study_design_scores_gemma":[0.00321439,0.000717809,0.07941839,0.0006005928,0.0003865924,0.00004247117,0.0005374598,0.04579126,0.6816632,0.1712273,0.01476064,0.001639855],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9246709,0.00009100787,0.07303534,0.0003806346,0.0004957547,0.0003976255,0.00006892129,0.0003014606,0.0005583411],"genre_scores_gemma":[0.7826254,0.00001537769,0.217069,0.00005663754,0.00007618321,0.00001336907,0.000008232404,0.00002471529,0.0001111228],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8441268,"threshold_uncertainty_score":0.9984247,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1103639924210811,"score_gpt":0.3339578366602562,"score_spread":0.2235938442391751,"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."}}