{"id":"W4281680688","doi":"10.3390/math10111937","title":"Estimation of Error Variance in Regularized Regression Models via Adaptive Lasso","year":2022,"lang":"en","type":"article","venue":"Mathematics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":7,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Manitoba","funders":"National Natural Science Foundation of China","keywords":"Lasso (programming language); Estimator; Variance (accounting); Elastic net regularization; Mean squared error; Model selection; Mathematics; Regression; Linear regression; Regression analysis; Computer science; Statistics; Algorithm","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":[0.01088721,0.001651189,0.00233515,0.001126078,0.0005221539,0.001594655,0.002332691,0.001538098,0.001102812],"category_scores_gemma":[0.02891456,0.0008120993,0.00129216,0.001478479,0.001990228,0.00225141,0.002715304,0.003509715,0.0003801213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006771316,"about_ca_system_score_gemma":0.001694533,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001563536,"about_ca_topic_score_gemma":0.001426255,"domain_scores_codex":[0.9907323,0.006329698,0.0003098405,0.0009865755,0.001333881,0.0003078133],"domain_scores_gemma":[0.9862264,0.01027681,0.001359225,0.00110716,0.0008724249,0.0001580244],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001659426,0.00007010549,0.001729794,0.000368735,0.0003182964,0.0002090185,0.0001542316,0.7988026,0.002652952,0.1174221,0.00321195,0.07489444],"study_design_scores_gemma":[0.00001103018,0.00002185815,0.0001371577,0.000016432,0.00001523504,0.00002967138,0.000008107542,0.9670926,0.0003897851,0.03169092,0.0005742141,0.00001284222],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.002202001,0.0002214284,0.9970279,0.0001335426,0.00002028314,0.00001160198,0.00002987956,0.00009591514,0.0002574555],"genre_scores_gemma":[0.3502026,0.001724902,0.6426845,0.0005781689,0.0005010458,0.0004808228,0.0007543463,0.0004421393,0.00263144],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.01088721,"threshold_uncertainty_score":0.05757773,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1463234895434989,"score_gpt":0.3833852389109052,"score_spread":0.2370617493674063,"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."}}