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 in all dimensions. We also derive its influence function, and compare it with sensitivity functions.breakdown value, influence function, regression depth
Several depths suitable for infinite-dimensional functional data that are available in the...
AbstractMotivated by the notion of regression depth (Rousseeuw and Hubert, 1996) we introduce thecat...
The statistical analysis of functional data is a growing need in many research areas. We propose a n...
In this paper we investigate the robustness properties of the deepest regression, a method for linea...
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...
While the halfspace depth has gained more and more popularity in the recent years as a robust estima...
Section 1 of the paper contains a general discussion of robustness. In Section 2 the influence funct...
Classical multivariate statistics measures the outlyingness of a point by its Mahalanobis distance f...
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...
AbstractMotivated by the notion of regression depth (Rousseeuw and Hubert, 1996) we introduce thecat...
The statistical analysis of functional data is a growing need in many research areas. We propose a n...
In this paper we investigate the robustness properties of the deepest regression, a method for linea...
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...
While the halfspace depth has gained more and more popularity in the recent years as a robust estima...
Section 1 of the paper contains a general discussion of robustness. In Section 2 the influence funct...
Classical multivariate statistics measures the outlyingness of a point by its Mahalanobis distance f...
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...
AbstractMotivated by the notion of regression depth (Rousseeuw and Hubert, 1996) we introduce thecat...
The statistical analysis of functional data is a growing need in many research areas. We propose a n...