{"id":"W2946175422","doi":"","title":"Consistent Pseudo-Maximum Likelihood Estimators and Groups of Transformations","year":2018,"lang":"en","type":"preprint","venue":"Munich Personal RePEc Archive (Ludwig Maximilian University of Munich)","topic":"Complex Systems and Time Series Analysis","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Heteroscedasticity; Estimator; Maximum likelihood; Consistency (knowledge bases); Mathematics; Applied mathematics; Transformation (genetics); Strong consistency; Statistics; Econometrics; Discrete mathematics; Biology","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":[0.01542796,0.001011683,0.001489981,0.002377642,0.0006581685,0.002442031,0.00277316,0.002710026,0.003075595],"category_scores_gemma":[0.07927706,0.001068693,0.001757319,0.001899217,0.00360329,0.004414114,0.003669161,0.003480445,0.001139435],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008801524,"about_ca_system_score_gemma":0.001558126,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0007199075,"about_ca_topic_score_gemma":0.0004769951,"domain_scores_codex":[0.9896377,0.007692608,0.0003645483,0.001113734,0.0009002721,0.0002910493],"domain_scores_gemma":[0.9620444,0.02701669,0.002554895,0.005579784,0.002477112,0.0003270696],"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":[0.0001397524,0.00007758085,0.003213031,0.0001573874,0.0001788391,0.000148057,0.0003773961,0.1490463,0.0009327623,0.7588409,0.002892008,0.08399586],"study_design_scores_gemma":[0.00004618657,0.00004840713,0.0004423401,0.00003460795,0.00001823255,0.00006109817,0.00004121913,0.4060957,0.0006457166,0.589878,0.002664243,0.00002423205],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.004278845,0.0001130061,0.994585,0.0001622639,0.00001780867,0.00002922992,0.000060708,0.0001257667,0.0006274272],"genre_scores_gemma":[0.2826723,0.0005806632,0.7093663,0.0004734225,0.0002119415,0.00087432,0.001363604,0.0005075404,0.003949946],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01542796,"threshold_uncertainty_score":0.08159184,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02156026787555418,"score_gpt":0.1931711893278159,"score_spread":0.1716109214522617,"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."}}