{"id":"W1983264599","doi":"10.1007/s10260-007-0082-4","title":"An online estimation scheme for a Hull–White model with HMM-driven parameters","year":2007,"lang":"en","type":"article","venue":"Statistical Methods & Applications","topic":"Stochastic processes and financial applications","field":"Economics, Econometrics and Finance","cited_by":36,"is_retracted":false,"has_abstract":false,"ca_institutions":"Western University","funders":"Natural Sciences and Engineering Research Council of Canada; European Commission","keywords":"Akaike information criterion; Hidden Markov model; Hidden semi-Markov model; Computer science; Markov model; Information Criteria; Estimation theory; Vasicek model; Short-rate model; Kalman filter; Markov process; Algorithm; Mathematics; Statistics; Econometrics; Model selection; Artificial intelligence; Variable-order Markov model; Machine learning; Interest rate","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":[],"consensus_categories":[],"category_scores_codex":[0.0007402358,0.00020153,0.0003719032,0.0001634598,0.0002789777,0.00006168898,0.0002990907,0.0001207435,0.0000238546],"category_scores_gemma":[0.0002902055,0.0002155541,0.00005781735,0.0004893129,0.0001713837,0.000149342,0.00002712176,0.000166381,0.00004673572],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008385017,"about_ca_system_score_gemma":0.00005824703,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003817158,"about_ca_topic_score_gemma":0.00003773343,"domain_scores_codex":[0.9981914,0.000008120459,0.0006866708,0.0006482488,0.00005665372,0.0004089255],"domain_scores_gemma":[0.9981925,0.0006144348,0.0003067119,0.0005380596,0.0001466609,0.0002015837],"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":[0.00004062767,0.0002605087,0.0001330981,0.00003240203,0.00001978445,1.797569e-7,0.00008380811,0.002307882,0.0001236861,0.9393655,0.00004219297,0.05759034],"study_design_scores_gemma":[0.0002653955,0.0001012965,0.001553109,0.000004542178,0.00002122417,0.000002071877,0.00003536058,0.4607677,0.00003428112,0.5348017,0.00221273,0.0002006575],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.0002884317,0.00007714961,0.9941412,0.0003874142,0.00002743906,0.001276199,0.002764011,0.0001089108,0.0009292501],"genre_scores_gemma":[0.03348118,0.000005122316,0.9637365,0.0003111285,0.0000741199,0.001703888,0.0005775512,0.00004615152,0.00006438855],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.4584598,"threshold_uncertainty_score":0.8790041,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.086730633645463,"score_gpt":0.3837666378897049,"score_spread":0.2970360042442419,"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."}}