Adaptively truncated maximum likelihood regression with asymmetric errors

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Robust estimation of intraweek periodicity in volatility and jump detection (2011, methodology): “As an alternative to the OLS and ML estimators, we propose to use the Truncated Maximum Likelihood (TML) estimator introduced by Marazzi and Yohai (2004). This estimator gives a zero weight to observations that are outliers according to the value of the ML loss function.”
Robust fitting of claim severity distributions and the method of trimmed moments (2009, background): “…in finance and economics (see, e.g., Dupuis and Victoria-Feser, 2006, Cowell and VictoriaFeser, 2006, 2007), geopedology (Vandewalle et al ., 2007), robust fitting of non-standard regression models (Marazzi and Yohai, 2004, 2006), a general minimum-distance modeling approach by Scott (2001).”
Second-order nonlinear least squares estimation (2008, background): “They also attract much attention in (Bayesian) robust statistics (e.g., Azzalini and Capitanio 1999, Marazzi and Yohai 2004, Sahu et al. 2003).”
Robust estimation in the normal mixture model based on robust clustering (2008, methodology): “A similar approach was adopted by Marazzi and Yohai [14] in the univariate regression model.”
Robust estimation for linear regression with asymmetric errors (2005, background): “It is well known that M-estimates with unbounded ‰ are not robust for high leverage outliers. Marazzi and Yohai (2003) .”