{"id":"W3124627006","doi":"","title":"Spline Regression in the Presence of Categorical Predictors","year":2012,"lang":"en","type":"preprint","venue":"RePEc: Research Papers in Economics","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"McMaster University","funders":"Social Sciences and Humanities Research Council of Canada","keywords":"Categorical variable; Mathematics; Estimator; Kernel regression; Spline (mechanical); Regression; Kernel (algebra); Nonparametric regression; Econometrics; Sample (material); Kriging; Statistics; Regression analysis; Monte Carlo method; Engineering; Discrete mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.02161703,0.0009728442,0.002797417,0.00176317,0.0007461986,0.00237217,0.004175037,0.002498027,0.004006204],"category_scores_gemma":[0.08832061,0.0008287671,0.001959422,0.004004502,0.002555205,0.002910268,0.003045312,0.004737151,0.001266361],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007608471,"about_ca_system_score_gemma":0.001378756,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005164011,"about_ca_topic_score_gemma":0.003695765,"domain_scores_codex":[0.9896721,0.006887747,0.000336571,0.001461886,0.001252703,0.0003890549],"domain_scores_gemma":[0.9126126,0.07584937,0.00401475,0.004666216,0.002265951,0.0005911912],"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":[0.0004340499,0.0001414258,0.01801473,0.000614574,0.0003901328,0.00126039,0.0005533247,0.5478752,0.001667338,0.2990816,0.003561199,0.1264061],"study_design_scores_gemma":[0.00002614143,0.00006213853,0.001553468,0.00004661192,0.00004424797,0.0001604956,0.00006064134,0.8251212,0.000366032,0.1701458,0.00238096,0.00003223759],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.017681,0.0006702157,0.9797983,0.0004953803,0.00006622764,0.00001942293,0.0001594269,0.0002426921,0.0008673712],"genre_scores_gemma":[0.5426369,0.001596906,0.4455896,0.000361345,0.0004805031,0.0002549102,0.0009603417,0.0002878111,0.007831774],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02161703,"threshold_uncertainty_score":0.1143232,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.160136762698253,"score_gpt":0.4620325208060874,"score_spread":0.3018957581078344,"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."}}