MacroPCA: An All-in-One PCA Method Allowing for Missing Values as Well as Cellwise and Rowwise Outliers

@article{Hubert2019MacroPCAAA,
  title={MacroPCA: An All-in-One PCA Method Allowing for Missing Values as Well as Cellwise and Rowwise Outliers},
  author={M. Hubert and P. Rousseeuw and W. V. Bossche},
  journal={Technometrics},
  year={2019},
  volume={61},
  pages={459 - 473}
}
Abstract Multivariate data are typically represented by a rectangular matrix (table) in which the rows are the objects (cases) and the columns are the variables (measurements). When there are many variables one often reduces the dimension by principal component analysis (PCA), which in its basic form is not robust to outliers. Much research has focused on handling rowwise outliers, that is, rows that deviate from the majority of the rows in the data (e.g., they might belong to a different… Expand
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