%0 Journal Article %1 Templ:2011:ISR:1994460.1994646 %A Templ, Matthias %A Kowarik, Alexander %A Filzmoser, Peter %C Amsterdam, The Netherlands, The Netherlands %D 2011 %I Elsevier Science Publishers B. V. %J Comput. Stat. Data Anal. %K imputation missing regression robust statistics %P 2793--2806 %R 10.1016/j.csda.2011.04.012 %T Iterative stepwise regression imputation using standard and robust methods %U http://dx.doi.org/10.1016/j.csda.2011.04.012 %V 55 %X 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.

@article{Templ:2011:ISR:1994460.1994646, abstract = {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. }, acmid = {1994646}, added-at = {2011-08-15T13:05:14.000+0200}, address = {Amsterdam, The Netherlands, The Netherlands}, author = {Templ, Matthias and Kowarik, Alexander and Filzmoser, Peter}, biburl = {https://www.bibsonomy.org/bibtex/27a492bd4e73b4f54d1ca84f487487eae/vivion}, description = {Iterative stepwise regression imputation using standard and robust methods}, doi = {10.1016/j.csda.2011.04.012}, interhash = {b0419443074236013e3966b2a54de3ee}, intrahash = {7a492bd4e73b4f54d1ca84f487487eae}, issn = {0167-9473}, issue = {10}, issue_date = {October, 2011}, journal = {Comput. Stat. Data Anal.}, keywords = {imputation missing regression robust statistics}, month = {October}, numpages = {14}, pages = {2793--2806}, publisher = {Elsevier Science Publishers B. V.}, timestamp = {2011-08-15T13:05:14.000+0200}, title = {Iterative stepwise regression imputation using standard and robust methods}, url = {http://dx.doi.org/10.1016/j.csda.2011.04.012}, volume = 55, year = 2011 }