While the halfspace depth has gained more and more popularity in the recent years as a robust estimator of the mean, regression depth, despite being based on a similar concept, is still a relatively unknown method. The main goal of this paper was therefore to introduce the concept of robust depth to the reader, illustrate its geometric interpre- tation, and provide at least a basic overview of the findings that occurred within the individual researches. Finally, a small simulation study was conducted comparing the de- epest regression method with other selected methods commonly used in practice, namely the method of least absolute deviations and ordinary least squares method.
AbstractDeepest regression (DR) is a method for linear regression introduced by P. J. Rousseeuw and ...
AbstractIn this paper we investigate the robustness properties of the deepest regression, a method f...
AbstractIn this note we present a characterization of halfspace depth which relates it with well-kno...
While the halfspace depth has gained more and more popularity in the recent years as a robust estima...
For multivariate data, the halfspace depth function can be seen as a natural and affine equivariant ...
AbstractGlobal depth, tangent depth and simplicial depths for classical and orthogonal regression ar...
Statistical depth functions became well known nonparametric tool of multivariate data analyses. The ...
The ordinary least squares regression can be misleading when there are outliers, heteroscedasticity ...
In this paper we investigate the robustness properties of the deepest regression, a method for linea...
Classical multivariate statistics measures the outlyingness of a point by its Mahalanobis distance f...
Sparse partial robust M regression is introduced as a new regression method. It is the first dimensi...
summary:Generalised halfspace depth function is proposed. Basic properties of this depth function in...
Deepest regression (DR) is a method for linear regression introduced by P. J. Rousseeuw and M. Huber...
This dissertation examines the robust regression methods. The primary purpose of this work is to pro...
In this paper we investigate the robustness properties of the deepest regression, a method for linea...
AbstractDeepest regression (DR) is a method for linear regression introduced by P. J. Rousseeuw and ...
AbstractIn this paper we investigate the robustness properties of the deepest regression, a method f...
AbstractIn this note we present a characterization of halfspace depth which relates it with well-kno...
While the halfspace depth has gained more and more popularity in the recent years as a robust estima...
For multivariate data, the halfspace depth function can be seen as a natural and affine equivariant ...
AbstractGlobal depth, tangent depth and simplicial depths for classical and orthogonal regression ar...
Statistical depth functions became well known nonparametric tool of multivariate data analyses. The ...
The ordinary least squares regression can be misleading when there are outliers, heteroscedasticity ...
In this paper we investigate the robustness properties of the deepest regression, a method for linea...
Classical multivariate statistics measures the outlyingness of a point by its Mahalanobis distance f...
Sparse partial robust M regression is introduced as a new regression method. It is the first dimensi...
summary:Generalised halfspace depth function is proposed. Basic properties of this depth function in...
Deepest regression (DR) is a method for linear regression introduced by P. J. Rousseeuw and M. Huber...
This dissertation examines the robust regression methods. The primary purpose of this work is to pro...
In this paper we investigate the robustness properties of the deepest regression, a method for linea...
AbstractDeepest regression (DR) is a method for linear regression introduced by P. J. Rousseeuw and ...
AbstractIn this paper we investigate the robustness properties of the deepest regression, a method f...
AbstractIn this note we present a characterization of halfspace depth which relates it with well-kno...