Robust estimation for linear regression with asymmetric errors

The authors propose a new class of robust estimators for the parameters of a regression model in which the distribution of the error terms belongs to a class of exponential families including the log-gamma distribution. These estimates, which are a natural extension of the MM-estimates for ordinary regression, may combine simultaneously high asymptotic efficiency and a high breakdown point. The authors prove the consistency and derive the asymptotic normal distribution of these estimates. A Monte Carlo study allows them to assess the efficiency and robustness of these estimates for finite samples.