A robust PCR method for high‐dimensional regressors

By Mia Hubert, Sabine Verboven. 2003

99 citations recorded by the source corpus.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/e2a5e11da942e8f7f15ce7f624eba99cc81d8295

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. Peter Filzmoser
4. K. V. Branden
5. Klaus Nordhausen
6. Tor D. Wager
7. Matthew C. Keller
8. S. Van Aelst

Citations in the corpus

• Increased sensitivity in neuroimaging analyses using robust regression (2005, background): “Although several groups have developed robust versions of multivariate techniques (Hubert and Vanden Branden, 2003; Hubert and Verboven, 2003; Hubert et al., 2002), we focus here on the former, bmassively univariateQ approach.”
• ROBPCA: A New Approach to Robust Principal Component Analysis (2005, methodology): “…and a robust method for logistic regression (Rousseeuw and Christmann 2003) In addition, using ROBPCA in robust principal components regression (Hubert and Verboven2003) and robust partial least squares (Hubert and Vanden Branden 2003) has been investigated The ROBPCA method thus opens a door…”
• Robust methods for partial least squares regression (2003, methodology): “That is why we concentrate on the cross-validated RMSEk, which will be denoted by RMSECVk [24], [9].”
• Robust linear regression for high‐dimensional data: An overview (2020, background): “More details can be found for example, in Hubert and Verboven (2003) and Han and Liu (2013).”