Add to ORKGIn this paper we investigate the robustness properties of the deepest regression, a method for linear regression introduced by Rousseeuw and Hubert [6]. We show that the deepest regression functional is Fisher-consistent for the conditional median, and has a breakdown value of 13 in all dimensions. We also derive its influence function, and compare it with sensitivity functions.
Several depths suitable for infinite-dimensional functional data that are available in the...
We consider a simple through-the-origin linear regression example introduced by Rousseeuw, van Aelst...
In statistics, classical methods often heavily rely on assumptions which cannot always be met in pra...
AbstractIn this paper we investigate the robustness properties of the deepest regression, a method f...
In this paper we investigate the robustness properties of the deepest regression, a method for linea...
Deepest regression (DR) is a method for linear regression introduced by P. J. Rousseeuw and M. Huber...
AbstractDeepest regression (DR) is a method for linear regression introduced by P. J. Rousseeuw and ...
Summary: Lp-norm weighted depth functions are introduced and the local and global robustness of thes...
Recently the concept of regression depth has been introduced [1]. The deepest regression (DR) is a m...
Recently the concept of regression depth has been introduced [J. Am. Stat. Assoc. 94 (1999) 388]. Th...
Section 1 of the paper contains a general discussion of robustness. In Section 2 the influence funct...
While the halfspace depth has gained more and more popularity in the recent years as a robust estima...
Classical multivariate statistics measures the outlyingness of a point by its Mahalanobis distance f...
AbstractMotivated by the notion of regression depth (Rousseeuw and Hubert, 1996) we introduce thecat...
Location estimators induced from depth functions increasingly have been pursued and studied in the l...
Several depths suitable for infinite-dimensional functional data that are available in the...
We consider a simple through-the-origin linear regression example introduced by Rousseeuw, van Aelst...
In statistics, classical methods often heavily rely on assumptions which cannot always be met in pra...
AbstractIn this paper we investigate the robustness properties of the deepest regression, a method f...
In this paper we investigate the robustness properties of the deepest regression, a method for linea...
Deepest regression (DR) is a method for linear regression introduced by P. J. Rousseeuw and M. Huber...
AbstractDeepest regression (DR) is a method for linear regression introduced by P. J. Rousseeuw and ...
Summary: Lp-norm weighted depth functions are introduced and the local and global robustness of thes...
Recently the concept of regression depth has been introduced [1]. The deepest regression (DR) is a m...
Recently the concept of regression depth has been introduced [J. Am. Stat. Assoc. 94 (1999) 388]. Th...
Section 1 of the paper contains a general discussion of robustness. In Section 2 the influence funct...
While the halfspace depth has gained more and more popularity in the recent years as a robust estima...
Classical multivariate statistics measures the outlyingness of a point by its Mahalanobis distance f...
AbstractMotivated by the notion of regression depth (Rousseeuw and Hubert, 1996) we introduce thecat...
Location estimators induced from depth functions increasingly have been pursued and studied in the l...
Several depths suitable for infinite-dimensional functional data that are available in the...
We consider a simple through-the-origin linear regression example introduced by Rousseeuw, van Aelst...
In statistics, classical methods often heavily rely on assumptions which cannot always be met in pra...