{"id":"W2128828816","doi":"10.1080/07474930903451565","title":"Information-Theoretic Distribution Test with Application to Normality","year":2009,"lang":"en","type":"article","venue":"Econometric Reviews","topic":"Monetary Policy and Economic Impact","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Guelph","funders":"Social Sciences and Humanities Research Council of Canada; Pennsylvania State University","keywords":"Mathematics; Score test; Lagrange multiplier; Normality; Principle of maximum entropy; Applied mathematics; Exponential family; Fisher information; Maximum entropy probability distribution; Asymptotic distribution; Wald test; Empirical likelihood; Monte Carlo method; Statistics; Statistical hypothesis testing; Likelihood principle; Entropy (arrow of time); Likelihood function; Mathematical optimization; Estimation theory; Estimator; Quasi-maximum likelihood","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.01094303,0.0009509058,0.00172946,0.004224212,0.0005705245,0.001532953,0.002161061,0.001799266,0.005194101],"category_scores_gemma":[0.1011122,0.0003847051,0.00125857,0.002418053,0.004114812,0.00382907,0.002956296,0.00252806,0.0008091747],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00125828,"about_ca_system_score_gemma":0.001572265,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0006335845,"about_ca_topic_score_gemma":0.0003091752,"domain_scores_codex":[0.9919426,0.005088427,0.0003467275,0.0007246417,0.001636244,0.0002613944],"domain_scores_gemma":[0.9295534,0.06024481,0.002908437,0.003088132,0.003682077,0.0005231832],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001103811,0.00007757302,0.004766466,0.0002366535,0.0001391107,0.0004594675,0.0002004199,0.1239035,0.001542908,0.7801617,0.002668195,0.08573369],"study_design_scores_gemma":[0.00003681235,0.0001145851,0.001727806,0.00007483987,0.00003030606,0.0003060202,0.00004313505,0.401978,0.001593394,0.5917326,0.002310542,0.00005196892],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.009503945,0.0004276706,0.9857178,0.0004350665,0.000042044,0.00006630353,0.0001382587,0.0001705438,0.003498277],"genre_scores_gemma":[0.6451557,0.001341729,0.3465297,0.0005976056,0.0006132439,0.0008273994,0.0008990882,0.0001943065,0.003841097],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01094303,"threshold_uncertainty_score":0.05787301,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03232107141213998,"score_gpt":0.2292298418503698,"score_spread":0.1969087704382299,"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."}}