Robust regression and outlier detection
Robust regression and outlier detection
Proceedings of the 1998 conference on Advances in neural information processing systems II
High breakdown estimators for principal components: the projection-pursuit approach revisited
Journal of Multivariate Analysis
Robust PCA and classification in biosciences
Bioinformatics
Sequential imputation for missing values
Computational Biology and Chemistry
Predicting incomplete gene microarray data with the use of supervised learning algorithms
Pattern Recognition Letters
Detection of multivariate outliers in business survey data with incomplete information
Advances in Data Analysis and Classification
Exploring incomplete data using visualization techniques
Advances in Data Analysis and Classification
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Single imputation methods have been wide-discussed topics among researchers in the field of bioinformatics. One major shortcoming of methods proposed until now is the lack of robustness considerations. Like all data, gene expression data can possess outlying values. The presence of these outliers could have negative effects on the imputated values for the missing values. Afterwards, the outcome of any statistical analysis on the completed data could lead to incorrect conclusions. Therefore it is important to consider the possibility of outliers in the data set, and to evaluate how imputation techniques will handle these values. In this paper, a simulation study is performed to test existing techniques for data imputation in case outlying values are present in the data. To overcome some shortcomings of the existing imputation techniques, a new robust imputation method that can deal with the presence of outliers in the data is introduced. In addition, the robust imputation procedure cleans the data for further statistical analysis. Moreover, this method can be easily extended towards a multiple imputation approach by which the uncertainty of the imputed values is emphasised. Finally, a classification example illustrates the lack of robustness of some existing imputation methods and shows the advantage of the multiple imputation approach of the new robust imputation technique.