{"id":"W2010528099","doi":"10.3150/13-bej539","title":"Model comparison with composite likelihood information criteria","year":2014,"lang":"en","type":"article","venue":"Bernoulli","topic":"Financial Risk and Volatility Modeling","field":"Economics, Econometrics and Finance","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Quasi-maximum likelihood; Information Criteria; Sequence (biology); Maximum likelihood; Composite number; Likelihood function; Gaussian; Likelihood principle; Term (time)","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":[0.000320256,0.0001286083,0.0003052487,0.0001114769,0.0001242469,0.0001058319,0.0001466076,0.00008238849,0.00004075504],"category_scores_gemma":[0.00003284414,0.000135731,0.0000505869,0.0001124271,0.00002830789,0.0005608025,0.0000348792,0.0001283481,0.0005485811],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000401106,"about_ca_system_score_gemma":0.00001338028,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000268485,"about_ca_topic_score_gemma":0.00004622213,"domain_scores_codex":[0.9990193,0.00000791566,0.0004992147,0.0001863563,0.00004393881,0.0002433241],"domain_scores_gemma":[0.999388,0.00001805818,0.0002154103,0.0002611612,0.00004665625,0.00007070633],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000258885,0.0003225868,0.4301426,0.0001821351,0.00006294268,5.961027e-7,0.006373757,0.05459715,0.0001093173,0.4744071,0.004739248,0.02880368],"study_design_scores_gemma":[0.000444826,0.00007473319,0.01454124,0.00001727334,0.000004455526,8.389873e-7,0.000020434,0.9366468,0.00004806447,0.02443032,0.02356821,0.0002027551],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4562964,0.0001330502,0.5214504,0.0002331494,0.0001117215,0.000108761,0.00005315537,0.0000575105,0.02155582],"genre_scores_gemma":[0.9849137,0.00001820764,0.01451904,0.0003171093,0.00006166869,0.00001177306,0.00005434988,0.00001322835,0.00009094081],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8820497,"threshold_uncertainty_score":0.7051083,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03253913500784959,"score_gpt":0.2389609387960135,"score_spread":0.2064218037881639,"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."}}