{"id":"W2157130628","doi":"10.1002/cjs.10086","title":"Optimal estimation in surrogate outcome regression problems","year":2010,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Science Fund for Distinguished Young Scholars","keywords":"Outcome (game theory); Estimator; Statistics; Regression analysis; Regression; Propensity score matching; Mathematics; Covariance matrix; Econometrics; Computer science","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":true,"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":[0.02850611,0.001232909,0.003579903,0.001398892,0.0003934683,0.001927431,0.002110061,0.002324948,0.002026978],"category_scores_gemma":[0.1110438,0.001184997,0.001434076,0.001560661,0.002713972,0.002302017,0.003426242,0.002552431,0.0003285006],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001502469,"about_ca_system_score_gemma":0.002132203,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002206994,"about_ca_topic_score_gemma":0.0009221534,"domain_scores_codex":[0.9803057,0.01662877,0.0005198658,0.001120046,0.001035966,0.0003896327],"domain_scores_gemma":[0.9304134,0.059526,0.003830218,0.003010432,0.002551001,0.0006689682],"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":[0.0003863259,0.0001128453,0.002382881,0.0003822195,0.0002780185,0.0002445845,0.0001202886,0.7240844,0.0006347172,0.2355292,0.002055744,0.03378889],"study_design_scores_gemma":[0.00006706886,0.00005928296,0.0002688571,0.00003850596,0.00001985668,0.00003027681,0.0000138999,0.9156752,0.0003190322,0.08293692,0.0005573556,0.00001389767],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007135437,0.0002869329,0.9914863,0.0004196383,0.0000303145,0.00003365081,0.00004961013,0.0000637288,0.0004942947],"genre_scores_gemma":[0.4888777,0.0008037076,0.5061514,0.0003407355,0.0002327299,0.0005807457,0.0006849242,0.0001626599,0.00216547],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02850611,"threshold_uncertainty_score":0.1507565,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1028935983325023,"score_gpt":0.3649809593459968,"score_spread":0.2620873610134945,"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."}}