{"id":"W3212304234","doi":"","title":"Efficient estimation using regularized Jackknife IV estimator","year":2017,"lang":"en","type":"article","venue":"SSRN Electronic Journal","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University; Université de Montréal","funders":"","keywords":"Jackknife resampling; Estimator; Mathematics; Tikhonov regularization; Instrumental variable; Applied mathematics; Regularization (linguistics); Statistics; Mean squared error; Mathematical optimization; Computer science; Inverse problem; Mathematical analysis; Artificial intelligence","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":[],"consensus_categories":[],"category_scores_codex":[0.002811879,0.0001726189,0.0002903479,0.00006671477,0.001032374,0.0002604086,0.000412879,0.00008870814,0.00007386135],"category_scores_gemma":[0.005560199,0.000141618,0.0001018483,0.00004783352,0.0001167702,0.00008750711,0.00007259316,0.001038352,0.00002529059],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0005242302,"about_ca_system_score_gemma":0.001048072,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003259335,"about_ca_topic_score_gemma":0.00001703795,"domain_scores_codex":[0.9974856,0.0001324352,0.0003935988,0.0001960922,0.0003546272,0.001437593],"domain_scores_gemma":[0.9984417,0.0003167806,0.0005110517,0.0004647324,0.0001436707,0.0001220569],"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":[0.00003645087,0.00006347366,0.0001099954,0.00001658447,0.00005508151,0.000005940002,0.00004314846,0.0002869547,0.0009762517,0.96539,0.00002441895,0.03299173],"study_design_scores_gemma":[0.0004841157,0.00007633548,0.0003025151,0.00005747488,0.00005879545,0.0002592541,0.00006802699,0.300966,0.0001276526,0.697459,0.00001317206,0.0001276014],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2463128,0.00007875044,0.7522364,0.0002421429,0.0002080814,0.0001103419,0.000002436156,0.00002709754,0.0007820082],"genre_scores_gemma":[0.5763553,0.00002617465,0.4232092,0.00001387671,0.0001311641,0.0000025413,4.259365e-7,0.00002322755,0.0002380964],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.3300425,"threshold_uncertainty_score":0.7940291,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06707860909132249,"score_gpt":0.3910808373985399,"score_spread":0.3240022283072174,"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."}}