{"id":"W3165547170","doi":"10.1002/cjs.11616","title":"Quantile function regression and variable selection for sparse models","year":2021,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Quantile regression; Quantile; Estimator; Quantile function; Mathematics; Statistics; Binomial regression; Feature selection; Econometrics; Linear regression; Regression analysis; Computer science; Cumulative distribution function; Probability density function; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0005339292,0.00008625525,0.0002336086,0.00008086702,0.0001387625,0.00007242863,0.00004628687,0.00006477007,0.0002642759],"category_scores_gemma":[0.003879014,0.00007416162,0.00002552804,0.0001129084,0.00003984995,0.00008555029,0.000005138599,0.0001419031,8.2983e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006082915,"about_ca_system_score_gemma":0.0009020393,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002689203,"about_ca_topic_score_gemma":0.002209299,"domain_scores_codex":[0.9991135,0.00008695306,0.0003938123,0.000102766,0.0001195642,0.0001834058],"domain_scores_gemma":[0.9974896,0.001040535,0.0002258615,0.00007856373,0.0008412765,0.0003241868],"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":[0.00002312253,0.00001136206,0.0002440664,0.0000794747,0.00002534768,0.00001979275,0.00008751384,0.00005878713,0.0002503738,0.9576532,0.02796036,0.0135866],"study_design_scores_gemma":[0.0002960762,0.0001906538,0.0003043424,0.0001047448,0.0000965907,0.0001280244,0.0001153942,0.03566651,0.0001935895,0.9577038,0.005113844,0.00008648597],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002706975,0.0001439624,0.9957457,0.00007676475,0.0003992728,0.00006477333,0.0003556815,0.000002854729,0.0005040379],"genre_scores_gemma":[0.0483198,0.00002020809,0.9510933,0.00008689419,0.0001099679,0.000002019965,0.000007630079,0.00001471598,0.0003454669],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04561283,"threshold_uncertainty_score":0.4643822,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1383965863067766,"score_gpt":0.3316610625230124,"score_spread":0.1932644762162358,"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."}}