{"id":"W3182332900","doi":"10.1515/jtse-2022-0016","title":"Temporally Local Maximum Likelihood with Application to SIS Model","year":2023,"lang":"en","type":"article","venue":"Journal of Time Series Econometrics","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University; University of Toronto","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Estimator; M-estimator; Parametric statistics; Mathematics; Constant (computer programming); Inference; Maximum likelihood; Bridging (networking); Extremum estimator; Applied mathematics; Econometrics; Series (stratigraphy); Nonlinear system; Statistics; Parametric model; Computer science; Physics; Artificial intelligence","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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.00122595,0.0001914278,0.000619843,0.001769467,0.0001168644,0.0001014554,0.0003838581,0.0001237844,0.0001004859],"category_scores_gemma":[0.0002230209,0.0001987433,0.0001692962,0.002223091,0.00004795632,0.0006174053,0.00008965036,0.0002261203,0.001365332],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002236936,"about_ca_system_score_gemma":0.0001046045,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000037407,"about_ca_topic_score_gemma":0.00002047903,"domain_scores_codex":[0.9980567,0.000008337623,0.00114822,0.0003166821,0.00009129492,0.0003787296],"domain_scores_gemma":[0.9984075,0.00006184872,0.0007784257,0.0003189815,0.0001972821,0.0002359479],"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":[0.001002145,0.000446779,0.07352616,0.0001953345,0.0003641006,0.00004552453,0.001896609,0.8078922,0.0001176314,0.01991483,0.02131665,0.07328201],"study_design_scores_gemma":[0.001339584,0.001571486,0.01567736,0.00005519563,0.00003974188,0.00004847123,0.0003385129,0.7338413,0.0001698749,0.1682078,0.07779698,0.0009137195],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.296974,0.0005007617,0.6940333,0.001960776,0.0002135142,0.0002686415,0.0001467624,0.00006641117,0.005835846],"genre_scores_gemma":[0.9752048,0.0003763694,0.02229545,0.0002617622,0.0002041913,0.00001436871,0.00001757076,0.00006060194,0.001564878],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6782308,"threshold_uncertainty_score":0.9994122,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0227457845425279,"score_gpt":0.2117227593532234,"score_spread":0.1889769748106955,"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."}}