{"id":"W1511044732","doi":"","title":"Hot Deck Imputation for the Response Model","year":2005,"lang":"en","type":"article","venue":"Iowa State University Digital Repository (Iowa State University)","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":67,"is_retracted":false,"has_abstract":true,"ca_institutions":"Statistics Canada","funders":"National Agricultural Statistics Service; Iowa State University","keywords":"Imputation (statistics); Monte Carlo method; Estimator; Statistics; Computer science; Missing data; Variance (accounting); Deck; Mathematics; Econometrics; Applied mathematics; Engineering","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0002861534,0.0003518634,0.0003564532,0.0003523436,0.0008249432,0.000247459,0.0006921681,0.0001177288,0.00001233717],"category_scores_gemma":[0.0005045256,0.000336403,0.00026253,0.0005135896,0.0003707996,0.001195782,0.0002467361,0.0002719666,0.00002131994],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006502488,"about_ca_system_score_gemma":0.000357807,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000039604,"about_ca_topic_score_gemma":0.00005678959,"domain_scores_codex":[0.9978853,0.0002413873,0.0003202164,0.0005811581,0.0003923224,0.000579595],"domain_scores_gemma":[0.9949007,0.003552701,0.0002986848,0.0005057727,0.000438254,0.0003038829],"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":[0.04756617,0.002071311,0.002434223,0.0006285136,0.001919528,0.002818887,0.01743972,0.02179382,0.008801028,0.580466,0.04020651,0.2738543],"study_design_scores_gemma":[0.01144055,0.001882691,0.00633615,0.0003461672,0.001306327,0.0001816733,0.01405647,0.2399802,0.007169272,0.4104611,0.3030345,0.003804907],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":"theoretical_or_conceptual","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1599644,0.000012131,0.8314655,0.0002617019,0.000134792,0.0005739863,0.0006154155,0.0002069012,0.00676526],"genre_scores_gemma":[0.6765214,0.00007748422,0.2325895,0.0001353837,0.0001001983,0.000001479312,0.00002918711,0.00008630134,0.09045911],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.598876,"threshold_uncertainty_score":0.9999088,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03194455000218778,"score_gpt":0.2690507851653187,"score_spread":0.2371062351631309,"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."}}