{"id":"W1976451055","doi":"10.2307/3315966","title":"Adaptive estimation in partially linear autoregressive models","year":2000,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Autoregressive model; Nonparametric statistics; Kernel density estimation; STAR model; Kernel (algebra); Parametric model; Parametric statistics; Linear model; Applied mathematics; Mathematics; Estimation; Nonlinear autoregressive exogenous model; Econometrics; Computer science; Mathematical optimization; Statistics; Time series; Autoregressive integrated moving average; Estimator; Engineering","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":[0.003060767,0.0005193949,0.0009363042,0.0006247233,0.0002189422,0.0008748615,0.001087308,0.0007729546,0.0009586251],"category_scores_gemma":[0.01907793,0.0005215212,0.0007850552,0.0008032859,0.0008475597,0.001194658,0.001094027,0.00105534,0.000173956],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004607989,"about_ca_system_score_gemma":0.00070952,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004963451,"about_ca_topic_score_gemma":0.003137558,"domain_scores_codex":[0.997684,0.001481145,0.00009215336,0.0003117624,0.0003191949,0.0001116744],"domain_scores_gemma":[0.9916937,0.006385153,0.0006942988,0.000560626,0.0005887618,0.00007754935],"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":[0.0001237207,0.0000391351,0.002770544,0.0001247763,0.000212089,0.00009159467,0.0001369521,0.8366909,0.001905122,0.08502565,0.0009937702,0.07188581],"study_design_scores_gemma":[0.000005147042,0.00001069658,0.0003721894,0.000006082038,0.000008975376,0.000009226462,0.000005487542,0.9830661,0.0002091477,0.01603389,0.0002671723,0.000005872828],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.01998424,0.0001540026,0.9791502,0.0001299549,0.00001870912,0.00001173124,0.00003143881,0.0001274304,0.0003922686],"genre_scores_gemma":[0.8014538,0.0004284425,0.1958366,0.0001045971,0.00008201701,0.0001007139,0.0002777509,0.00006065894,0.001655556],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004963451,"threshold_uncertainty_score":0.01618713,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1116650947750087,"score_gpt":0.3370469616608487,"score_spread":0.22538186688584,"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."}}