Multiple imputation of missing data continues to be a topic of considerable interest and importance to applied researchers. In this article, the ice package for multiple imputation by chained equations (also known as fully conditional specification) is further updated. Special attention is paid to categorical variables. The relationship between ice and the new multiple-imputation system in Stata 11 is clarified
Multiple imputation is a popular way to handle missing data. Automated procedures are widely availab...
Multiple imputation is a popular way to handle missing data. Automated procedures are widely availab...
Multiple imputation is a recommended method for handling incomplete data problems. One of the barrie...
Multiple imputation of missing data continues to be a topic of considerable interest and importance ...
Missing data are a common occurrence in real datasets. For epidemiological and prognostic factors st...
Multiple imputation of missing data continues to be a topic of considerable interest and importance ...
Multiple imputation of missing data continues to be a topic of considerable interest and importance ...
Missing data are a common occurrence in real datasets. For epidemiological and prognostic factors st...
Royston (2004) introduced mvis, an implementation for Stata of MICE, a method of multiple multivaria...
This article describes a substantial update to mvis, which brings it more closely in line with the f...
Royston (2004) introduced mvis, an implementation for Stata of MICE, a method of multiple multivaria...
Background: Various methods for multiple imputations of missing values are available in statistical ...
Following the seminal publications of Rubin about thirty years ago, statisticians have become increa...
Following the seminal publications of Rubin about thirty years ago, statisticians have become increa...
Multiple imputation is a popular way to handle missing data. Automated procedures are widely availab...
Multiple imputation is a popular way to handle missing data. Automated procedures are widely availab...
Multiple imputation is a popular way to handle missing data. Automated procedures are widely availab...
Multiple imputation is a recommended method for handling incomplete data problems. One of the barrie...
Multiple imputation of missing data continues to be a topic of considerable interest and importance ...
Missing data are a common occurrence in real datasets. For epidemiological and prognostic factors st...
Multiple imputation of missing data continues to be a topic of considerable interest and importance ...
Multiple imputation of missing data continues to be a topic of considerable interest and importance ...
Missing data are a common occurrence in real datasets. For epidemiological and prognostic factors st...
Royston (2004) introduced mvis, an implementation for Stata of MICE, a method of multiple multivaria...
This article describes a substantial update to mvis, which brings it more closely in line with the f...
Royston (2004) introduced mvis, an implementation for Stata of MICE, a method of multiple multivaria...
Background: Various methods for multiple imputations of missing values are available in statistical ...
Following the seminal publications of Rubin about thirty years ago, statisticians have become increa...
Following the seminal publications of Rubin about thirty years ago, statisticians have become increa...
Multiple imputation is a popular way to handle missing data. Automated procedures are widely availab...
Multiple imputation is a popular way to handle missing data. Automated procedures are widely availab...
Multiple imputation is a popular way to handle missing data. Automated procedures are widely availab...
Multiple imputation is a recommended method for handling incomplete data problems. One of the barrie...