{"id":"W4280608081","doi":"10.1002/cjs.11700","title":"Missing data analysis with sufficient dimension reduction","year":2022,"lang":"en","type":"article","venue":"Canadian Journal of Statistics","topic":"Statistical Methods and Inference","field":"Mathematics","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Natural Science Foundation of China","keywords":"Estimator; Sufficient dimension reduction; Dimension (graph theory); Dimensionality reduction; Subspace topology; Missing data; Covariate; Mathematics; Reduction (mathematics); Moment (physics); Applied mathematics; Dimensional reduction; Statistics; Computer science; Combinatorics; Mathematical analysis; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0009528116,0.00008963623,0.0002726023,0.0003414548,0.000342361,0.00005538578,0.0003126316,0.00001740388,0.0009349077],"category_scores_gemma":[0.001254129,0.00007546271,0.00002743302,0.000576539,0.00008984876,0.0000556188,0.00004072946,0.0002857644,9.322974e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001922188,"about_ca_system_score_gemma":0.0009601515,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001299214,"about_ca_topic_score_gemma":0.002048455,"domain_scores_codex":[0.9986997,0.0001813868,0.0004057355,0.0001488164,0.0003529385,0.0002114621],"domain_scores_gemma":[0.9981745,0.0004640991,0.0003615403,0.0003556704,0.0002538057,0.0003903634],"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":[0.0001982918,0.0002714291,0.006568154,0.0001205601,0.001825263,0.0026693,0.004538523,0.007169359,0.000324716,0.7274693,0.1444163,0.1044287],"study_design_scores_gemma":[0.00241824,0.003935304,0.02546485,0.0002556321,0.01132786,0.003524986,0.01120533,0.1641508,0.0002433724,0.7364506,0.03937678,0.001646209],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.02126283,0.00008707355,0.9758825,0.0002674083,0.0002904703,0.00005679909,0.001911627,0.000003745622,0.0002375644],"genre_scores_gemma":[0.3355356,0.000002841473,0.6642537,0.00004066008,0.00004426201,5.769245e-7,0.00004881166,0.00001161633,0.00006202298],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.3142728,"threshold_uncertainty_score":0.9999784,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1476966875454268,"score_gpt":0.3433709350643394,"score_spread":0.1956742475189126,"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."}}