Statistical analysis with missing data
Statistical analysis with missing data
Sequential imputation for missing values
Computational Biology and Chemistry
Principal component analysis for data containing outliers and missing elements
Computational Statistics & Data Analysis
Avoiding bias due to perfect prediction in multiple imputation of incomplete categorical variables
Computational Statistics & Data Analysis
Imputation of missing values for compositional data using classical and robust methods
Computational Statistics & Data Analysis
Complex Surveys: A Guide to Analysis Using R
Complex Surveys: A Guide to Analysis Using R
Modern Applied Statistics with S
Modern Applied Statistics with S
Recursive partitioning on incomplete data using surrogate decisions and multiple imputation
Computational Statistics & Data Analysis
Exploring incomplete data using visualization techniques
Advances in Data Analysis and Classification
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Imputation of missing values is one of the major tasks for data pre-processing in many areas. Whenever imputation of data from official statistics comes into mind, several (additional) challenges almost always arise, like large data sets, data sets consisting of a mixture of different variable types, or data outliers. The aim is to propose an automatic algorithm called IRMI for iterative model-based imputation using robust methods, encountering for the mentioned challenges, and to provide a software tool in R. This algorithm is compared to the algorithm IVEWARE, which is the ''recommended software'' for imputations in international and national statistical institutions. Using artificial data and real data sets from official statistics and other fields, the advantages of IRMI over IVEWARE-especially with respect to robustness-are demonstrated.