{"id":"W3133814030","doi":"10.1080/03610926.2021.1890125","title":"Linear approximate Bayes estimator for regression parameter with an inequality constraint","year":2021,"lang":"en","type":"article","venue":"Communication in Statistics- Theory and Methods","topic":"Advanced Statistical Methods and Models","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"","keywords":"Mathematics; Estimator; Bayes' theorem; Applied mathematics; Constraint (computer-aided design); Mean squared error; Linear regression; Statistics; Bayesian probability","routes":{"ca_aff":true,"ca_fund":false,"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":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.006092026,0.0002003015,0.0004634138,0.00004836757,0.0002205653,0.00004397994,0.0001844965,0.0001022126,0.00004212045],"category_scores_gemma":[0.01403297,0.000155322,0.00002784205,0.0001132171,0.0004150123,0.0001353574,0.0001211369,0.0002665345,2.84508e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002610508,"about_ca_system_score_gemma":0.00007693288,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00000313898,"about_ca_topic_score_gemma":0.00001334995,"domain_scores_codex":[0.9939446,0.004848999,0.0005172906,0.0003487383,0.000107809,0.000232492],"domain_scores_gemma":[0.9734142,0.02509176,0.0002216071,0.0008677911,0.0002742874,0.0001302976],"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":[0.0003097081,0.0001351566,0.00003760776,0.0002304048,0.00001810784,0.000003711153,0.000658224,0.00001742091,0.0003464439,0.8248038,0.00001331037,0.1734261],"study_design_scores_gemma":[0.0007639889,0.0001312821,0.00008030251,0.0002162115,0.00006139496,0.00001826436,0.0009118341,0.0378585,0.002355033,0.9570939,0.0002928209,0.0002164588],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002384518,0.0003504115,0.9961335,0.00008617663,0.0000383974,0.000404887,0.0003456381,0.00004671057,0.0002097584],"genre_scores_gemma":[0.01370845,0.0001385092,0.985604,0.0001126221,0.00001279276,0.0001934886,0.0001083254,0.00003415823,0.00008764138],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.1732097,"threshold_uncertainty_score":0.9942722,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2038141274851704,"score_gpt":0.5436401536883851,"score_spread":0.3398260262032148,"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."}}