{"id":"W2901132294","doi":"10.1002/cjs.11466","title":"Rank‐based inference with responses missing not at random","year":2018,"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":"","keywords":"Missing data; Estimator; Outlier; Statistics; Robustness (evolution); Statistical inference; Computer science; Inference; Robust regression; Monte Carlo method; Robust statistics; Regression analysis; Mathematics; Econometrics; Data mining; Artificial intelligence","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":[0.05698861,0.001030774,0.002666435,0.001767495,0.001074055,0.001857343,0.003693286,0.002278768,0.008921868],"category_scores_gemma":[0.2165894,0.0007295649,0.001205187,0.003048784,0.003493039,0.002642347,0.002596711,0.004036994,0.001753847],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001657009,"about_ca_system_score_gemma":0.004280508,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005746486,"about_ca_topic_score_gemma":0.005695709,"domain_scores_codex":[0.9316688,0.05864059,0.001503962,0.00323301,0.004117155,0.0008365677],"domain_scores_gemma":[0.7456286,0.2080625,0.01090572,0.02770251,0.006817006,0.0008836521],"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":[0.001349645,0.000265266,0.01367455,0.00125746,0.0007517265,0.001091481,0.0007740571,0.1386693,0.001632112,0.5556436,0.02422081,0.26067],"study_design_scores_gemma":[0.0002114332,0.0002617396,0.002614269,0.000221388,0.0001487806,0.0003674481,0.000191267,0.4060993,0.002149176,0.5788301,0.008819015,0.00008608648],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"methods","genre_scores_codex":[0.007971362,0.0003689407,0.9877676,0.001127615,0.0001005944,0.0001602765,0.0006302139,0.0003069698,0.001566435],"genre_scores_gemma":[0.3949331,0.0007994338,0.5942693,0.001192505,0.0004126423,0.001108726,0.001836988,0.0002366438,0.00521058],"genre_candidate":"methods","genre_consensus":"methods","teacher_disagreement_score":0.05698861,"threshold_uncertainty_score":0.3013882,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1321928907379329,"score_gpt":0.3601690560726994,"score_spread":0.2279761653347665,"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."}}