INVESTIGADORES
YOHAI Victor Jaime
artículos
Título:
High finite-sample efficiency and robustness based on distance-constrained maximum likelihood .
Autor/es:
RICARDO A. MARONNA; VICTOR J. YOHAI
Revista:
COMPUTATIONAL STATISTICS AND DATA ANALYSIS
Editorial:
ELSEVIER SCIENCE BV
Referencias:
Lugar: Amsterdam; Año: 2015 vol. 83 p. 262 - 274
ISSN:
0167-9473
Resumen:
Good robust estimators can be tuned to combine a high breakdown point and a specified asymptotic efficiency at a central model. This happens in regression with MM- and ô -estimators among others. However, the finite-sample efficiency of these estimators can be much lower than the asymptotic one. To overcome this drawback, an approach isproposed for parametric models, which is based on a distance between parameters. Givena robust estimator, the proposed one is obtained by maximizing the likelihood under the constraint that the distance is less than a given threshold. For the linear model with normal errors, simulations show that the proposed estimator attains a finite-sample efficiency close to one while improving the robustness of the initial estimator. The same approach also shows good results in the estimation of multivariate location and scatter.