{"id":"W2160558412","doi":"10.1016/s0167-9473(01)00048-2","title":"A modified score function estimator for multinomial logistic regression in small samples","year":2002,"lang":"en","type":"article","venue":"Computational Statistics & Data Analysis","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":94,"is_retracted":false,"has_abstract":false,"ca_institutions":"McGill University; Lunenfeld-Tanenbaum Research Institute; University of Toronto","funders":"","keywords":"Mathematics; Statistics; Multinomial logistic regression; Logistic regression; Covariate; Binomial regression; Estimator; Econometrics; Multinomial distribution","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01552172,0.001020516,0.002206663,0.002357103,0.0006961557,0.002384865,0.004429705,0.00266533,0.006324461],"category_scores_gemma":[0.07944721,0.000931214,0.001554889,0.002999879,0.001773699,0.003630094,0.003304383,0.002766151,0.002388596],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001098504,"about_ca_system_score_gemma":0.002652226,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00285505,"about_ca_topic_score_gemma":0.003587758,"domain_scores_codex":[0.9899832,0.006919423,0.0003897899,0.0009635321,0.001504905,0.0002390661],"domain_scores_gemma":[0.9692248,0.02180794,0.001215932,0.003645111,0.003549937,0.0005561186],"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":[0.0006264541,0.000251036,0.00877787,0.0005787921,0.00073715,0.0003199633,0.0003108311,0.09501627,0.006043774,0.2780012,0.01340932,0.5959274],"study_design_scores_gemma":[0.0002152859,0.0001768473,0.003430567,0.00009867924,0.0001961159,0.0004131875,0.00004662215,0.8284575,0.001528365,0.1563603,0.008985551,0.00009089214],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.003440814,0.0001948409,0.9954437,0.0001694534,0.00007050079,0.00004931978,0.0000750017,0.0001963309,0.0003600713],"genre_scores_gemma":[0.09522243,0.0004601012,0.89759,0.0003567845,0.0004316424,0.0005892157,0.0006716985,0.000376419,0.00430175],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01552172,"threshold_uncertainty_score":0.0820877,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4420553852036066,"score_gpt":0.4284358865853111,"score_spread":0.01361949861829548,"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."}}