Robust Regression and Outlier Detection¶
Rousseeuw, P. J., & Leroy, A. M. (1987). Robust Regression and Outlier Detection. Wiley.
Cited by¶
1 citation across 1 artifact.
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Primes¶
- Outlier Leverage
- It also predicts that aggregation rules with breakdown point near zero — the mean, the variance, the OLS slope — become increasingly leverage-vulnerable as distributions grow more heavy-tailed, while rules with breakdown point near one-half — the median, the MAD, the Theil–Sen slope — trade some efficiency on Gaussian data for robustness to leverage on real-world data.
This sourceBreakdown points of estimators (OLS at 1/n, median/MAD near 1/2, high-breakdown methods), and robust alternatives resistant to leverage.
- It also predicts that aggregation rules with breakdown point near zero — the mean, the variance, the OLS slope — become increasingly leverage-vulnerable as distributions grow more heavy-tailed, while rules with breakdown point near one-half — the median, the MAD, the Theil–Sen slope — trade some efficiency on Gaussian data for robustness to leverage on real-world data.
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