{"id":"W4306359307","doi":"10.1007/s10479-022-04993-w","title":"Robust multivariate adaptive regression splines under cross-polytope uncertainty: an application in a natural gas market","year":2022,"lang":"en","type":"article","venue":"Annals of Operations Research","topic":"Risk and Portfolio Optimization","field":"Decision Sciences","cited_by":35,"is_retracted":false,"has_abstract":false,"ca_institutions":"University of Calgary","funders":"","keywords":"Robustification; Multivariate adaptive regression splines; Computer science; Robust optimization; Mathematical optimization; Curse of dimensionality; Interpretability; Uncertainty quantification; Mars Exploration Program; Univariate; Multivariate statistics; Exploit; Nonparametric regression; Regression analysis; Machine learning; Mathematics; Artificial intelligence; Outlier","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":[0.004401116,0.0007321914,0.002055197,0.0009247719,0.0005082636,0.001507156,0.001471084,0.002401829,0.002104253],"category_scores_gemma":[0.01382736,0.0005693671,0.001220185,0.001661849,0.001170709,0.0016896,0.001316612,0.002244501,0.0001495489],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009275635,"about_ca_system_score_gemma":0.001106515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01033395,"about_ca_topic_score_gemma":0.004613427,"domain_scores_codex":[0.9991416,0.0004824959,0.00002860639,0.0001336924,0.0001186466,0.00009496387],"domain_scores_gemma":[0.9932776,0.005286644,0.0004702767,0.000267305,0.0005411231,0.0001569536],"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":[0.00007592136,0.0000442828,0.0003997924,0.00003080179,0.00002636371,0.00006277262,0.00002533345,0.9693735,0.0004536429,0.01715644,0.0003129712,0.01203828],"study_design_scores_gemma":[0.000004713919,0.00001374424,0.00008164182,0.00000208942,0.000003716254,0.000004363806,0.000003312234,0.9973761,0.00005169613,0.002375856,0.00007816378,0.000004550163],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1409692,0.0006904897,0.8546483,0.000585197,0.0000752058,0.00003471065,0.00008521463,0.0003933545,0.002518323],"genre_scores_gemma":[0.9173951,0.0006538964,0.07729875,0.00007623836,0.0000980656,0.00005415888,0.000107927,0.0001403909,0.004175562],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01033395,"threshold_uncertainty_score":0.02327561,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4183602756074331,"score_gpt":0.5421433808573288,"score_spread":0.1237831052498957,"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."}}