Robust estimation for regression with asymmetric errors

ANA BIANCO; MARTA GARCÍA BEN; VÍCTOR J. YOHAI

CANADIAN JOURNAL OF STATISTICS-REVUE CANADIENNE DE STATISTIQUE

Statistical Society of Canada

Lugar: Montreal, Quebec, Canada; Año: 2005 vol. 33 p. 511 - 528

0319-5724

 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 sample