{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007392324,0.0005320667,0.001253385,0.001601326,0.0005006928,0.001088565,0.001943744,0.001166524,0.004656316],"category_scores_gemma":[0.03522332,0.000464481,0.0007019961,0.001657628,0.000884001,0.001624201,0.001784144,0.001375592,0.002223614],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007532089,"about_ca_system_score_gemma":0.001328404,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001155808,"about_ca_topic_score_gemma":0.001036806,"domain_scores_codex":[0.9966691,0.001643015,0.0001314183,0.0003728681,0.001039114,0.0001444755],"domain_scores_gemma":[0.9885408,0.006461519,0.001075658,0.001634001,0.002136279,0.0001517526],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0002196499,0.00016349,0.0124345,0.0005595747,0.0002548201,0.0002831945,0.0004209414,0.1684064,0.01489758,0.241395,0.007372558,0.5535922],"study_design_scores_gemma":[0.00007028891,0.0001563867,0.006357925,0.000203444,0.000108695,0.0009622911,0.0000762749,0.8018311,0.008971324,0.1653886,0.01575715,0.0001165973],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004755619,0.0002584087,0.9939277,0.00009495036,0.00003794396,0.00002567118,0.00005534256,0.00008713409,0.0007572952],"genre_scores_gemma":[0.1627824,0.0007360925,0.8306257,0.000226925,0.0001731631,0.0002945919,0.0004978086,0.0001336862,0.004529719],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007392324,"threshold_uncertainty_score":0.03909481,"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."}}