Influence functions and outlier detection under the common principal components model: A robust approach

By Graciela Boente, A. Pires, I. M. Rodrigues. 2002

55 citations recorded by the source corpus.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/a12fc0f6ff193d03fad2221dce8cebd26cdd7679

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Statistics and Probability

1. Mia Hubert
2. Peter J. Rousseeuw
3. S. Van Aelst
4. K. V. Branden
5. Christophe Croux
6. Anne Ruiz-Gazen
7. Peter Filzmoser
8. Andrea Cerioli

Citations in the corpus

• ROBPCA: A New Approach to Robust Principal Component Analysis (2005, background): “This idea has also been generalized to common principal components (Boente, Pires, and Rodrigues 2002).”
• High breakdown estimators for principal components: the projection-pursuit approach revisited (2005, methodology): “Filzmoser (1999) applied it to a geostatistical problem, Boente et al. (2002) in the context of common principal components, and Gather et al. (2001) for robust sliced inverse regression.”
• High-Breakdown Robust Multivariate Methods (2008, methodology): “See Hampel et al. (1986, Chapter 6) for an overview.”