{"id":"W1845661996","doi":"10.6000/1929-6029.2015.04.03.7","title":"Multiple Imputation by Fully Conditional Specification for Dealing with Missing Data in a Large Epidemiologic Study","year":2015,"lang":"en","type":"article","venue":"International Journal of Statistics in Medical Research","topic":"Statistical Methods and Bayesian Inference","field":"Mathematics","cited_by":465,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"National Institutes of Health; U.S. President’s Emergency Plan for AIDS Relief; Centers for Disease Control and Prevention; Georgia State University","keywords":"Categorical variable; Missing data; Imputation (statistics); Computer science; Data mining; Multivariate statistics; Statistics; Econometrics; Mathematics; Machine learning","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.4574323852140853,"score_gpt":0.598124388630447,"score_spread":0.1406920034163617,"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."}}