{"id":"W4302009424","doi":"10.1016/j.spl.2022.109693","title":"Theoretical properties of Bayesian Student-<mml:math xmlns:mml=\"http://www.w3.org/1998/Math/MathML\" display=\"inline\" id=\"d1e420\" altimg=\"si2.svg\"><mml:mi>t</mml:mi></mml:math> linear regression","year":2022,"lang":"en","type":"article","venue":"Statistics & Probability Letters","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":5,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université de Montréal","funders":"Fonds de recherche du Québec – Nature et technologies; Natural Sciences and Engineering Research Council of Canada","keywords":"Mathematics; Bayesian probability; Linear regression; Regression; Robustness (evolution); Applied mathematics; Calculus (dental); Algorithm; Statistics; Medicine","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":[0.03862699,0.00186847,0.003485754,0.006010372,0.002864355,0.007942378,0.00712215,0.005214003,0.0275076],"category_scores_gemma":[0.206108,0.002125188,0.003177511,0.004890847,0.00980896,0.0167985,0.006197984,0.008458007,0.005505641],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.006878851,"about_ca_system_score_gemma":0.005339456,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.004648312,"about_ca_topic_score_gemma":0.004844863,"domain_scores_codex":[0.9840392,0.007295994,0.0006172367,0.003141337,0.00348012,0.001426023],"domain_scores_gemma":[0.7714376,0.1927219,0.009083542,0.01141404,0.01182544,0.003517523],"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":[0.00002726248,0.00003126782,0.0007425428,0.00007014366,0.00003331831,0.00003676405,0.0002341431,0.006649234,0.00008690429,0.9821044,0.002873107,0.007110921],"study_design_scores_gemma":[0.00002027357,0.0000187058,0.0002957213,0.0000721966,0.00002123813,0.00007923213,0.00006461634,0.06738533,0.0001733805,0.9304648,0.001378343,0.00002619582],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.01800569,0.001034401,0.9534804,0.004729243,0.000159528,0.0001387621,0.0009481236,0.0004369901,0.02106692],"genre_scores_gemma":[0.6535792,0.005924122,0.274483,0.004514151,0.002534186,0.00274913,0.004920294,0.001533239,0.04976268],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03862699,"threshold_uncertainty_score":0.2042814,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03953097163700605,"score_gpt":0.3183988694316784,"score_spread":0.2788678977946724,"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."}}