{"id":"W2128677065","doi":"10.1109/icassp.1989.266901","title":"Structured maximum likelihood autoregressive parameter estimation","year":2003,"lang":"en","type":"article","venue":"International Conference on Acoustics, Speech, and Signal Processing","topic":"Fault Detection and Control Systems","field":"Engineering","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Autoregressive model; STAR model; Estimation theory; Nonlinear autoregressive exogenous model; Maximum likelihood; Mathematics; Maximum likelihood sequence estimation; Applied mathematics; Computer science; SETAR; Statistics; Algorithm; Time series; Autoregressive integrated moving average","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":[0.002245568,0.001263889,0.001656415,0.001329899,0.000464957,0.001675591,0.001946952,0.001474571,0.004065225],"category_scores_gemma":[0.01003781,0.001092698,0.001329409,0.001736087,0.0007306879,0.002125739,0.001712908,0.002151085,0.002841261],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005326429,"about_ca_system_score_gemma":0.001481056,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001471249,"about_ca_topic_score_gemma":0.002010314,"domain_scores_codex":[0.9981778,0.0008542357,0.00008531736,0.0003028504,0.0005009858,0.00007883803],"domain_scores_gemma":[0.9971629,0.001851972,0.0002279798,0.0003271324,0.0003855965,0.00004433745],"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":[0.0001796021,0.0001041372,0.00128974,0.0004620402,0.0002816295,0.0002523534,0.0002112755,0.4717402,0.006926362,0.0874387,0.01166642,0.4194476],"study_design_scores_gemma":[0.0000163877,0.00001874324,0.0001716731,0.00002487712,0.00001361405,0.00005747246,0.0000108271,0.9636428,0.001209009,0.031395,0.003422755,0.00001683016],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0003730672,0.00007713419,0.9989604,0.00002714858,0.00001150313,0.00001017444,0.00004593536,0.000226498,0.0002681862],"genre_scores_gemma":[0.05698649,0.000418108,0.9374604,0.0001110831,0.0001286474,0.0002485647,0.001118497,0.0003191962,0.003208997],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.004065225,"threshold_uncertainty_score":0.01359951,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01497753227879025,"score_gpt":0.2510771895765211,"score_spread":0.2360996572977309,"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."}}