{"id":"W35581981","doi":"10.1007/s11424-013-1113-x","title":"Selecting an adaptive sequence for computing recursive M-estimators in multivariate linear regression models","year":2013,"lang":"en","type":"article","venue":"Journal of Systems Science and Complexity","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":1,"is_retracted":false,"has_abstract":false,"ca_institutions":"York University","funders":"","keywords":"Estimator; Sequence (biology); Multivariate statistics; Algorithm; Computer science; Adaptive estimator; Multivariate adaptive regression splines; Linear regression; Linear model; Bayesian multivariate linear regression; Applied mathematics; Mathematics; Mathematical optimization; Statistics; Machine learning","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":[0.005809697,0.0009069791,0.001206506,0.001580108,0.0006168096,0.001000735,0.002279081,0.002180884,0.002298095],"category_scores_gemma":[0.04377816,0.001137038,0.0009375307,0.001207728,0.001135395,0.002208832,0.002166062,0.002344398,0.0009086525],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007933748,"about_ca_system_score_gemma":0.001782786,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002334212,"about_ca_topic_score_gemma":0.003912665,"domain_scores_codex":[0.9979702,0.001165597,0.0001600863,0.0002973728,0.0003069087,0.00009984183],"domain_scores_gemma":[0.9824992,0.01402487,0.0008394216,0.001076803,0.001244756,0.0003148984],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0006267017,0.0002554806,0.005030408,0.0003730854,0.0001998034,0.0001945933,0.000343845,0.4688718,0.02021161,0.07554937,0.002073346,0.4262699],"study_design_scores_gemma":[0.00002319913,0.00005907784,0.0003394748,0.00002635534,0.00001663246,0.00004186395,0.0000158572,0.9819617,0.00280816,0.01424271,0.0004476049,0.0000173432],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007838093,0.00008060306,0.9916891,0.00004791078,0.00001712128,0.00001878749,0.00001187863,0.0001813943,0.0001151559],"genre_scores_gemma":[0.1548473,0.0002021043,0.843589,0.00009906461,0.00007032884,0.0002180275,0.0001451437,0.0001616261,0.0006673684],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.005809697,"threshold_uncertainty_score":0.03072494,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4160903285908822,"score_gpt":0.4788160256457443,"score_spread":0.06272569705486203,"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."}}