{"id":"W2204079748","doi":"10.1111/sjos.12198","title":"Doubly Robust Inference for the Distribution Function in the Presence of Missing Survey Data","year":2015,"lang":"en","type":"article","venue":"Scandinavian Journal of Statistics","topic":"Survey Sampling and Estimation Techniques","field":"Mathematics","cited_by":10,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal","funders":"Agence Nationale de la Recherche","keywords":"Imputation (statistics); Missing data; Estimator; Mathematics; Statistics; Inference; Robustness (evolution); Econometrics; Computer science; 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":"codex-gemma-dda1882f352a","candidate_categories":["metaresearch"],"consensus_categories":[],"category_scores_codex":[0.007569219,0.0000790267,0.0001825299,0.0000438129,0.00008027454,0.00006495386,0.0005178555,0.00003883272,0.000004038138],"category_scores_gemma":[0.01583873,0.0000456657,0.00002116392,0.0001999477,0.00009478812,0.0001755664,0.00003878926,0.0001750736,3.043109e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004275604,"about_ca_system_score_gemma":0.000146599,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001869528,"about_ca_topic_score_gemma":0.0001338056,"domain_scores_codex":[0.9986331,0.0003105927,0.000517771,0.0000801646,0.0003394072,0.0001189297],"domain_scores_gemma":[0.9916126,0.006732668,0.0005789707,0.0003435471,0.0006892243,0.00004302519],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"not_applicable","study_design_gemma":"theoretical_or_conceptual","study_design_scores_codex":[0.002981756,0.000888406,0.2206451,0.0007798672,0.0002800237,0.00003230312,0.008862609,0.00826239,0.0000633805,0.06073707,0.5156837,0.1807833],"study_design_scores_gemma":[0.002037462,0.001145446,0.2709554,0.0009932156,0.0003116051,0.0001285556,0.002344039,0.1027072,0.000198038,0.6177441,0.001117974,0.0003169488],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.005293817,0.0001116422,0.9926769,0.0001556678,0.0002133462,0.0001738671,0.001352234,0.000006116177,0.00001645516],"genre_scores_gemma":[0.9011452,0.00001881525,0.09853517,0.00001147808,0.00005934143,0.000003241446,0.0002090872,0.000007538269,0.00001018199],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8958513,"threshold_uncertainty_score":0.9924513,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4282603565689823,"score_gpt":0.4277465425077435,"score_spread":0.0005138140612387931,"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."}}