{"id":"W1964907750","doi":"10.1081/sac-120017860","title":"Improved Estimation of Coefficient Vector in a Regression Model","year":2003,"lang":"en","type":"article","venue":"Communications in Statistics - Simulation and Computation","topic":"Random Matrices and Applications","field":"Mathematics","cited_by":21,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Windsor; Saint Mary's University","funders":"","keywords":"Estimator; Mathematics; Mean squared error; Invariant estimator; Statistics; Minimax estimator; James–Stein estimator; Efficient estimator; Stein's unbiased risk estimate; Minimum-variance unbiased estimator; Consistent estimator; Applied mathematics","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.007205953,0.0008363911,0.001188296,0.001010024,0.0002523337,0.001070052,0.001478871,0.001100637,0.001996067],"category_scores_gemma":[0.02966627,0.0004850656,0.0006433247,0.0007203738,0.001252563,0.002438638,0.001908541,0.001329681,0.0003832268],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006850798,"about_ca_system_score_gemma":0.001177496,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00214075,"about_ca_topic_score_gemma":0.001560486,"domain_scores_codex":[0.9964809,0.00190428,0.0001191407,0.0004970171,0.0008339358,0.0001646718],"domain_scores_gemma":[0.9887196,0.0073273,0.001179692,0.001164831,0.00142714,0.0001814225],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.0003453003,0.0001045862,0.01143697,0.0002389315,0.0002442435,0.0003015746,0.000148854,0.7528015,0.007262268,0.1025453,0.001615057,0.1229554],"study_design_scores_gemma":[0.00001263028,0.00008100185,0.001254288,0.00001526263,0.00002277696,0.00004950089,0.000009824717,0.9792677,0.001764941,0.01712396,0.000383089,0.00001508826],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.043324,0.0001310777,0.9555222,0.0001737377,0.00001274427,0.00002281724,0.00003271496,0.0001456454,0.0006351063],"genre_scores_gemma":[0.8302131,0.0002946855,0.1658918,0.0001144094,0.0000788509,0.00007202031,0.0002377091,0.00005360779,0.003043799],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.007205953,"threshold_uncertainty_score":0.03810912,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.137476362885203,"score_gpt":0.4549370491650011,"score_spread":0.3174606862797981,"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."}}