A class of robust and fully efficient regression estimators

By Daniel Gervini, Victor J. Yohai. 2002

193 citations recorded by the source corpus.

Subfields: Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/4196bf0ff0b1a686095698af24fd400f393cff1e

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. Alfio Marazzi
2. Ana M. Bianco
3. M. G. Ben
4. Victor J. Yohai
5. Graciela Boente
6. Jianhui Zhou
7. Xuming He

Citations in the corpus

• Robust estimation for linear regression with asymmetric errors (2005, background): “Other proposal that have simultaneously these two properties are the τ−estimates (Zamar and Yohai (1988) ) CM-estimates (Mendes and Tyler (1996)), and the adaptive estimates proposed in Gervini and Yohai (2002).”
• Robust linear regression: A review and comparison (2014, unknown): “Gervini and Yohai (2002) proposed a new class of robust regression method called robust and efficient weighted least squares estimator (REWLSE).”
• Adaptively truncated maximum likelihood regression with asymmetric errors (2004, methodology): “Remark 1. Gervini and Yohai (2002) propose an adaptive cut-off procedure based on the difference F+ 0 (z) − F+ n (z) to measure the proportion of outliers.”