{"id":"W2120481695","doi":"10.1111/j.1467-9469.2009.00641.x","title":"Single‐Index Additive Vector Autoregressive Time Series Models","year":2009,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":15,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Department of Atmospheric Sciences, Texas A and M University; King Abdullah University of Science and Technology; CMG Reservoir Simulation Foundation; National Science Foundation","keywords":"Autoregressive model; Mathematics; Series (stratigraphy); Smoothing; Model selection; Vector autoregression; Econometrics; Nonlinear system; Time series; Autoregressive integrated moving average; STAR model; Index (typography); Applied mathematics; Statistics; Computer science","routes":{"ca_aff":false,"ca_fund":true,"ca_venue":false,"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":[0.0003808368,0.0002476219,0.0005907494,0.0001326406,0.0001170752,0.00009330081,0.0002635684,0.00009701726,0.0006487079],"category_scores_gemma":[0.002390829,0.0001965545,0.00009520995,0.0001422366,0.0001919019,0.0002752857,0.00002394433,0.0003657741,0.00001788067],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001400664,"about_ca_system_score_gemma":0.00012618,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000193682,"about_ca_topic_score_gemma":0.000001131496,"domain_scores_codex":[0.9980145,0.0001788537,0.0007471224,0.0001681057,0.0005341938,0.0003572194],"domain_scores_gemma":[0.996822,0.00120054,0.0007715846,0.0001914032,0.0007232329,0.0002912251],"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":[0.0004123743,0.0003447331,0.00009258351,0.00005227136,0.0001336613,0.0008524253,0.001305958,0.00003587002,0.0008063088,0.832958,0.04008425,0.1229215],"study_design_scores_gemma":[0.0005821902,0.001846352,0.002468705,0.0004102241,0.0001170474,0.0003394976,0.0001544765,0.002681605,0.0007263778,0.9901214,0.000290126,0.0002620426],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002418494,0.00009531934,0.9905955,0.0002787801,0.000330127,0.0001352256,0.001165933,0.00002817747,0.004952404],"genre_scores_gemma":[0.2806808,0.00003654034,0.7180486,0.00009754411,0.0002888698,0.00000144582,0.000008184066,0.00002661493,0.0008113791],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.2782623,"threshold_uncertainty_score":0.801526,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05530894893085468,"score_gpt":0.3230555685678049,"score_spread":0.2677466196369502,"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."}}