{"id":"W3123179483","doi":"10.1016/j.econlet.2013.11.017","title":"Testing for normality in linear regression models using regression and scale equivariant estimators","year":2013,"lang":"en","type":"article","venue":"Economics Letters","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":2,"is_retracted":false,"has_abstract":false,"ca_institutions":"","funders":"McGill University","keywords":"Mathematics; Monte Carlo method; Estimator; Statistics; Equivariant map; Regression analysis; Linear regression; Null (SQL); Null hypothesis; Heteroscedasticity; Robust regression; Econometrics; Statistical hypothesis testing; Regression; Regression diagnostic; Polynomial regression; Computer science; Data mining","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.04457562,0.001477076,0.002697142,0.002336893,0.0009889106,0.003745549,0.003412656,0.002421691,0.00345729],"category_scores_gemma":[0.2969906,0.001043575,0.003179181,0.00215762,0.006618681,0.007594041,0.003885757,0.004606663,0.0006906419],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009443702,"about_ca_system_score_gemma":0.002532996,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001437805,"about_ca_topic_score_gemma":0.0006866241,"domain_scores_codex":[0.9387615,0.04490835,0.002737995,0.006833328,0.005262505,0.001496276],"domain_scores_gemma":[0.5632095,0.3885242,0.01201079,0.02590944,0.008188008,0.002158081],"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":[0.001591076,0.0007130391,0.0986182,0.0005352337,0.002682118,0.001573186,0.002001764,0.1431753,0.007909439,0.5388422,0.003510557,0.1988479],"study_design_scores_gemma":[0.0001399219,0.0005490849,0.01703551,0.00007079648,0.0002081918,0.000496975,0.0005299745,0.6029302,0.002969152,0.3735236,0.001429749,0.0001167778],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.07584344,0.0001737354,0.9219849,0.0004271893,0.00007336374,0.00004978694,0.00009328208,0.0004519686,0.0009021932],"genre_scores_gemma":[0.8905746,0.0002874406,0.1058481,0.0002786207,0.0002648282,0.0002668376,0.0005256,0.0004053605,0.001548478],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.04457562,"threshold_uncertainty_score":0.2357412,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1754268499315464,"score_gpt":0.3982463814104884,"score_spread":0.222819531478942,"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."}}