{"id":"W2102865692","doi":"10.1017/s0266466619000124","title":"SMOOTHED QUANTILE REGRESSION PROCESSES FOR BINARY RESPONSE MODELS","year":2019,"lang":"en","type":"article","venue":"Econometric Theory","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Quantile; Mathematics; Quantile regression; Estimator; Heteroscedasticity; Nonparametric statistics; Quantile function; Smoothing; Binary number; Representation (politics); Econometrics; Linearization; Function (biology); Applied mathematics; Statistics; Nonlinear system; Probability distribution; Moment-generating function","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":["metaresearch","insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.002464652,0.0001490293,0.0003305796,0.0003954729,0.00006607472,0.00003346081,0.0002299685,0.00008774846,0.001515678],"category_scores_gemma":[0.01192076,0.0001126227,0.0000715108,0.0006400163,0.00004615901,0.0001479903,0.00005789407,0.00008737965,0.0001342807],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00003979586,"about_ca_system_score_gemma":0.00009157957,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":9.472621e-7,"about_ca_topic_score_gemma":1.622337e-7,"domain_scores_codex":[0.9988077,0.0002508874,0.0003133615,0.0003044068,0.00007536433,0.0002482508],"domain_scores_gemma":[0.9798248,0.01945743,0.0001651971,0.0003754035,0.0001010376,0.00007616649],"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":[0.001437076,0.0001198685,0.0001455914,0.0003720128,0.00002302403,8.57558e-7,0.0002342016,0.00002063975,0.0001968525,0.9778348,0.0012008,0.01841429],"study_design_scores_gemma":[0.0004865892,0.0003317851,0.0005809833,0.00007299956,0.00001694312,0.000001890627,0.0001788845,0.003933837,0.000664309,0.9921204,0.001428497,0.000182842],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6663071,0.0007059264,0.3196991,0.0001297941,0.0003337861,0.0007401961,0.0001249252,0.0001048879,0.01185425],"genre_scores_gemma":[0.8385412,0.00002788124,0.1537124,0.00009352827,0.00005628305,0.00009815039,0.000003880481,0.0000413448,0.007425432],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.172234,"threshold_uncertainty_score":0.9993971,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1134742628853326,"score_gpt":0.3746827283439117,"score_spread":0.2612084654585791,"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."}}