{"id":"W26522724","doi":"10.1038/onc.2015.403","title":"Absolute penalty and shrinkage estimation strategies in linear and partially linear models with correlated errors","year":2013,"lang":"en","type":"article","venue":"Oncogene","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"University of Windsor","keywords":"Estimator; Lasso (programming language); Mathematics; Linear model; Linear regression; Shrinkage; Monte Carlo method; Statistics; Extremum estimator; Applied mathematics; M-estimator; Computer science","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":[0.01008645,0.002400381,0.002704733,0.001621135,0.001013219,0.001886905,0.003510826,0.003593164,0.005124923],"category_scores_gemma":[0.02511227,0.001979257,0.002255084,0.001702932,0.002038382,0.002502587,0.003971165,0.004263448,0.001621022],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00139198,"about_ca_system_score_gemma":0.003929218,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009461818,"about_ca_topic_score_gemma":0.009108828,"domain_scores_codex":[0.9964484,0.002247756,0.0002273366,0.0005359569,0.0003212166,0.000219258],"domain_scores_gemma":[0.9850567,0.01157867,0.0008232579,0.0007080002,0.00152913,0.0003041993],"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":[0.0001802348,0.00007797721,0.000867294,0.0003125147,0.0002489329,0.0001740319,0.000152204,0.8675311,0.0006518618,0.03251266,0.002301843,0.09498937],"study_design_scores_gemma":[0.0000144425,0.00003438884,0.00009956142,0.00001958474,0.00001227387,0.00001542853,0.00001269966,0.9869803,0.0001510979,0.01184056,0.0008063925,0.00001319989],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002739965,0.0004758775,0.9954785,0.0003188868,0.00005917665,0.00004477149,0.00006761241,0.0002930017,0.0005221943],"genre_scores_gemma":[0.1991274,0.001404776,0.7835996,0.0004613514,0.0003914105,0.001255573,0.00148419,0.0006503511,0.01162542],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01008645,"threshold_uncertainty_score":0.05334288,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06425359231416836,"score_gpt":0.3446744608700242,"score_spread":0.2804208685558559,"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."}}