{"id":"W2787055726","doi":"10.1007/s11518-018-5359-7","title":"Analysis of the Impact of Sample Size, Attribute Variance and Within-Sample Choice Distribution on the Estimation Accuracy of Multinomial Logit Models Using Simulated Data","year":2018,"lang":"en","type":"article","venue":"Journal of Systems Science and Systems Engineering","topic":"Economic and Environmental Valuation","field":"Economics, Econometrics and Finance","cited_by":3,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary; University of Waterloo","funders":"Wuhan University","keywords":"Sample size determination; Multinomial logistic regression; Sample (material); Statistics; Econometrics; Variance (accounting); Mixed logit; Mathematics; Multinomial distribution; Set (abstract data type); A priori and a posteriori; Sample variance; Logit; Computer science; Logistic regression; Economics","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":[0.002976116,0.00009119308,0.0004306287,0.0001516609,0.0001044577,0.00005928137,0.0003196541,0.00004358389,0.000002327323],"category_scores_gemma":[0.002042351,0.00006111716,0.00006447941,0.0005237262,0.000155214,0.000671068,0.00008171293,0.00007195706,1.63471e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001844613,"about_ca_system_score_gemma":0.00004740063,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002975255,"about_ca_topic_score_gemma":0.00000428737,"domain_scores_codex":[0.9986316,0.00002583724,0.00094332,0.0001618307,0.0001207004,0.0001167205],"domain_scores_gemma":[0.9970689,0.0007698472,0.00167108,0.0003252616,0.0001245349,0.00004039581],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000005669932,0.00001124551,0.1071175,0.0000354607,0.0001214889,3.250284e-8,0.00020828,0.8902152,0.001268658,0.00100025,0.000002697922,0.00001359095],"study_design_scores_gemma":[0.000169941,0.0000588829,0.1399098,0.0001171137,0.00005061492,0.00000354971,0.00009470505,0.8593739,0.0001248626,0.00004300095,0.000002585488,0.00005107857],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8902001,0.0002190137,0.1086558,0.00000981394,0.0002348682,0.000160358,0.0005142609,0.000001527484,0.000004235727],"genre_scores_gemma":[0.999651,0.00002210814,0.0002661393,0.000001396414,0.00004813852,4.939318e-7,0.00000497682,0.000004535971,0.000001265039],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.1094508,"threshold_uncertainty_score":0.4497716,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1897376399688788,"score_gpt":0.2898517625294079,"score_spread":0.1001141225605291,"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."}}