{"id":"W3125762866","doi":"","title":"Multinomial Probit Estimation Without Nuisance Parameters","year":2003,"lang":"ca","type":"article","venue":"SSRN Electronic Journal","topic":"Spatial and Panel Data Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Concordia University","funders":"","keywords":"Multinomial probit; Covariance; Mathematics; Statistics; Econometrics; Monte Carlo method; Multinomial distribution; Rank (graph theory); Law of total covariance; Probit; Covariance matrix; Probit model; Estimation of covariance matrices; Covariance intersection","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":["metaepi_narrow","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.003089116,0.0004023314,0.0007695252,0.0004112351,0.0004652313,0.0003444411,0.0004738265,0.0002520791,0.0004814485],"category_scores_gemma":[0.0005408609,0.0004531483,0.0004332402,0.0005268658,0.0001064046,0.0006276491,0.00003505592,0.002202135,0.001241673],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001577534,"about_ca_system_score_gemma":0.00111425,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007113539,"about_ca_topic_score_gemma":0.001053536,"domain_scores_codex":[0.9949192,0.0001370894,0.00122472,0.0006389188,0.0001277193,0.002952356],"domain_scores_gemma":[0.9979993,0.00007382325,0.001178138,0.0004620635,0.00008102258,0.0002057298],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.000262888,0.0005641247,0.1433722,0.00007671675,0.002284816,0.0000150557,0.0009423482,0.0150213,0.00004956626,0.7904406,0.0005975843,0.04637282],"study_design_scores_gemma":[0.006600208,0.001472343,0.005412816,0.0001655546,0.0006470979,0.001011651,0.001802216,0.1087264,0.0003002891,0.8341024,0.03726003,0.002498968],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6563592,0.02885104,0.3006319,0.001333916,0.002946417,0.0007655233,0.0002421417,0.0000656999,0.008804136],"genre_scores_gemma":[0.9825269,0.009509351,0.003255971,0.0001587387,0.0002925286,0.00001456629,0.00004926576,0.0000580655,0.004134621],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.3261677,"threshold_uncertainty_score":0.999792,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01913770563275063,"score_gpt":0.2242989729702754,"score_spread":0.2051612673375248,"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."}}