{"id":"W3133949540","doi":"10.1002/cjs.11590","title":"Optimal subsampling for linear quantile regression models","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":14,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China; Natural Science Foundation of Shanghai","keywords":"Estimator; Differentiable function; Mathematics; Quantile; Sampling (signal processing); Mean squared error; Applied mathematics; Mathematical optimization; Statistics; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0185563,0.0007113852,0.001541994,0.001143749,0.0005741762,0.0009065174,0.001910847,0.0009797707,0.002178788],"category_scores_gemma":[0.04507278,0.0005541827,0.001134605,0.0010205,0.001317277,0.001184233,0.001302708,0.001375914,0.0002990616],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001295994,"about_ca_system_score_gemma":0.001545065,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008746678,"about_ca_topic_score_gemma":0.005897101,"domain_scores_codex":[0.9858506,0.01135569,0.0002938449,0.0008895037,0.001307103,0.000303257],"domain_scores_gemma":[0.9743363,0.01956163,0.001521661,0.002514836,0.001770439,0.0002950574],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0005449507,0.0002753943,0.007341741,0.0003687906,0.0003213451,0.0002484137,0.0002579701,0.6405966,0.002123986,0.1971525,0.004303453,0.1464648],"study_design_scores_gemma":[0.00004150983,0.00008001924,0.0007845435,0.0000226781,0.00003157745,0.00002847142,0.00002396743,0.9590349,0.0007416194,0.03801642,0.001178914,0.00001541997],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009374655,0.0002908483,0.9894813,0.0001256786,0.00003485783,0.00007260861,0.00005514976,0.0001418968,0.0004228505],"genre_scores_gemma":[0.4918421,0.0005201258,0.5045488,0.0003116368,0.0001771159,0.0004592428,0.0005155425,0.0001154217,0.001510057],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.0185563,"threshold_uncertainty_score":0.09813625,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2117338134352754,"score_gpt":0.3900390015315592,"score_spread":0.1783051880962839,"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."}}