Robust regression and outlier detection
Robust regression and outlier detection
Influence function and efficiency of the minimum covariance determinant scatter matrix estimator
Journal of Multivariate Analysis
High breakdown estimators for principal components: the projection-pursuit approach revisited
Journal of Multivariate Analysis
A comparative study on diabetes disease diagnosis using neural networks
Expert Systems with Applications: An International Journal
Robust PCA for skewed data and its outlier map
Computational Statistics & Data Analysis
Principal component regression for data containing outliers and missing elements
Computational Statistics & Data Analysis
A generalization of Tyler's M-estimators to the case of incomplete data
Computational Statistics & Data Analysis
PPCA-based missing data imputation for traffic flow volume: a systematical approach
IEEE Transactions on Intelligent Transportation Systems
Detecting influential observations in principal components and common principal components
Computational Statistics & Data Analysis
Detecting influential observations in Kernel PCA
Computational Statistics & Data Analysis
Robust clusterwise linear regression through trimming
Computational Statistics & Data Analysis
Imputation of missing values for compositional data using classical and robust methods
Computational Statistics & Data Analysis
Asymptotic expansion of the minimum covariance determinant estimators
Journal of Multivariate Analysis
Iterative stepwise regression imputation using standard and robust methods
Computational Statistics & Data Analysis
Unsupervised feature relevance analysis applied to improve ECG heartbeat clustering
Computer Methods and Programs in Biomedicine
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Two approaches are presented to perform principal component analysis (PCA) on data which contain both outlying cases and missing elements. At first an eigendecomposition of a covariance matrix which can deal with such data is proposed, but this approach is not fit for data where the number of variables exceeds the number of cases. Alternatively, an expectation robust (ER) algorithm is proposed so as to adapt the existing methodology for robust PCA to data containing missing elements. According to an extensive simulation study, the ER approach performs well for all data sizes concerned. Using simulations and an example, it is shown that by virtue of the ER algorithm, the properties of the existing methods for robust PCA carry through to data with missing elements.