{"id":"W3010371357","doi":"10.1080/01621459.2020.1737079","title":"Targeted Inference Involving High-Dimensional Data Using Nuisance Penalized Regression","year":2020,"lang":"en","type":"article","venue":"Journal of the American Statistical Association","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"National Institute on Drug Abuse; National Human Genome Research Institute; National Institute of Mental Health; National Institutes of Health; National Science Foundation","keywords":"Nuisance parameter; Estimator; Inference; Statistic; Econometrics; Statistics; Nuisance; Mathematics; Computer science; Coherence (philosophical gambling strategy); Statistical inference; Regression; Regression 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.02602974,0.001549285,0.002799788,0.00174099,0.0008198375,0.001969364,0.004408878,0.002634926,0.001989869],"category_scores_gemma":[0.1155504,0.001019409,0.002174768,0.001937494,0.003109969,0.003860225,0.004210913,0.004289057,0.0006657804],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001140991,"about_ca_system_score_gemma":0.002402812,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002547899,"about_ca_topic_score_gemma":0.002572472,"domain_scores_codex":[0.9840946,0.01216304,0.0004707012,0.001666881,0.001281269,0.0003235392],"domain_scores_gemma":[0.9063506,0.08083415,0.003715606,0.006049722,0.002488557,0.0005614194],"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":[0.0003488063,0.0001535496,0.005907638,0.0005213624,0.0005416138,0.0007294632,0.0005520592,0.593183,0.004267873,0.2785743,0.002061575,0.1131588],"study_design_scores_gemma":[0.00002237092,0.00005146865,0.0004048352,0.0000290946,0.00002670548,0.0000611438,0.00001998775,0.9331787,0.0007582633,0.06484544,0.0005806011,0.00002147033],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.001859401,0.00007230788,0.9977776,0.00007135378,0.000007778041,0.0000196614,0.00001568847,0.00006542599,0.0001107252],"genre_scores_gemma":[0.2011545,0.0005662401,0.7955261,0.0003416272,0.0001226521,0.0004405714,0.000311016,0.0001929832,0.001344173],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.02602974,"threshold_uncertainty_score":0.13766,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1664995748513426,"score_gpt":0.4235532931007584,"score_spread":0.2570537182494158,"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."}}