{"id":"W1501316563","doi":"","title":"Finite-Sample Moments of the MLE for the Binary Logit Model","year":2008,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Victoria","funders":"","keywords":"Estimator; Mathematics; Logit; Statistics; Covariate; Sample (material); Mixed logit; Econometrics; Logistic regression; Applied mathematics; Function (biology); Binary number; Physics","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":[0.001677143,0.0002703949,0.0007426133,0.0004190627,0.0003264584,0.00007989082,0.001851419,0.0003423917,0.0001214065],"category_scores_gemma":[0.0018506,0.0002160044,0.0005443706,0.0002486177,0.0004042932,0.00009241383,0.001562068,0.0008942906,0.00001916362],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003692638,"about_ca_system_score_gemma":0.0002416194,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001551809,"about_ca_topic_score_gemma":0.0003912404,"domain_scores_codex":[0.9973304,0.00006473005,0.001092918,0.0008160329,0.0000971393,0.0005987276],"domain_scores_gemma":[0.9951012,0.002291316,0.0005988099,0.00183901,0.00008822344,0.00008142635],"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":[0.0001120961,0.00019078,0.02172724,0.0001402005,0.0003505104,0.000001401637,0.0004993164,0.960391,0.00000617114,0.004527015,0.0005364544,0.01151777],"study_design_scores_gemma":[0.0004916202,0.00004943708,0.003140843,0.00004565095,0.00001637158,5.449191e-7,0.0001425181,0.9350934,0.00003432207,0.03608949,0.02460394,0.0002918515],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7531449,0.007394048,0.01462038,0.01091125,0.004993842,0.01190103,0.05857516,0.0001326271,0.1383267],"genre_scores_gemma":[0.961023,0.03357148,0.001006226,0.0001791938,0.0001830157,0.0005864557,0.0002993875,0.00006439748,0.003086829],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2078781,"threshold_uncertainty_score":0.8808404,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1328578448868554,"score_gpt":0.3100945888803877,"score_spread":0.1772367439935323,"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."}}