{"id":"W1514590158","doi":"10.3386/w10447","title":"A Jackknife Estimator for Tracking Error Variance of Optimal Portfolios Constructed Using Estimated Inputs1","year":2004,"lang":"en","type":"article","venue":"National Bureau of Economic Research","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Kellogg's (Canada)","funders":"","keywords":"Jackknife resampling; Estimator; Statistics; Variance (accounting); Mathematics; Econometrics; Minimum-variance unbiased estimator; Tracking error; Computer science; Economics; Artificial intelligence; Accounting","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":[0.0008909737,0.0001210309,0.000262147,0.0005171946,0.0001868366,0.00008584023,0.0002311837,0.000110867,0.000128962],"category_scores_gemma":[0.0006526877,0.0001301246,0.00009362761,0.0003323688,0.000226305,0.0008076057,0.00007753552,0.0001360833,0.00001749161],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002082675,"about_ca_system_score_gemma":0.0004372105,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001122523,"about_ca_topic_score_gemma":0.00003342525,"domain_scores_codex":[0.9985287,0.000009125538,0.0005279725,0.000271102,0.0003792456,0.0002839075],"domain_scores_gemma":[0.9982523,0.0001208751,0.0003385636,0.000122311,0.001149541,0.00001638064],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002502532,0.0001313075,0.002788539,0.0002976989,0.00005295171,0.000002245616,0.00001974681,0.07720949,0.007955416,0.9098561,0.0004616172,0.0009746263],"study_design_scores_gemma":[0.004220878,0.00009083656,0.02456308,0.0005428992,0.0000588004,0.00001422662,0.0001825339,0.5094309,0.005857466,0.4542933,0.0003657764,0.0003792481],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9825497,0.00006701719,0.005538595,0.0003693562,0.000299473,0.0007187272,0.0001023383,0.00004386048,0.01031096],"genre_scores_gemma":[0.9920542,0.000003078088,0.007295971,0.00002614517,0.0004047285,0.00003275546,0.0001450257,0.00002063921,0.00001740903],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4555628,"threshold_uncertainty_score":0.5306329,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.288630631833755,"score_gpt":0.4589883131717963,"score_spread":0.1703576813380413,"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."}}