© 2014 Taylor & Francis. We discuss two recently proposed adaptations of the well-known Stahel–Donoho estimator of multivariate location and scatter for high-dimensional data. The first adaptation adjusts the calculation of the outlyingness of the observations while the second adaptation allows to give separate weights to each of the components of an observation. Both adaptations address the possibility that in higher dimensions most observations can be contaminated in at least one of its components. We then combine the two approaches in a new method and investigate its performance in comparison to the previously proposed methods.status: publishe
AbstractIn this article, we study a class of projection based scatter depth functions proposed by Zu...
All known robust location and scale estimators with high breakdown point for multivariate samples ar...
All known robust location and scale estimators with high breakdown point for multivariate sample&apo...
The Stahel-Donoho estimator is dened as a weighted mean and covariance, where the weight of each obs...
We deal with the equivariant estimation of scatter and location for p-dimensional data, giving empha...
Data sets can be very large, highly multidimensional and of mixed quality. This thesis provides fea...
The depth of multivariate data can be used to construct weighted means as robust estimators of locat...
It is known that the efficiency at the normal of M estimates of multivariate location and scatter in...
For the problem of robust estimation of multivariate location and shape, defilllng S-estimators usin...
Consider a p-dimensional sample X = (x1,..., xn) of size n. In this paper we will concentrate on Sta...
A powerful procedure for outlier detection and robust estimation of shape and location with multivar...
This article derives the influence function of the Stahel-Donoho estimator of multivariate location ...
The Stahel-Donoho estimator is defined as a weighted mean and covariance, where each observation rec...
The authors are to be commended for bringing the critical problem of cellwise outliers to the attent...
AbstractThis article proposes a reweighted estimator of multivariate location and scatter, with weig...
AbstractIn this article, we study a class of projection based scatter depth functions proposed by Zu...
All known robust location and scale estimators with high breakdown point for multivariate samples ar...
All known robust location and scale estimators with high breakdown point for multivariate sample&apo...
The Stahel-Donoho estimator is dened as a weighted mean and covariance, where the weight of each obs...
We deal with the equivariant estimation of scatter and location for p-dimensional data, giving empha...
Data sets can be very large, highly multidimensional and of mixed quality. This thesis provides fea...
The depth of multivariate data can be used to construct weighted means as robust estimators of locat...
It is known that the efficiency at the normal of M estimates of multivariate location and scatter in...
For the problem of robust estimation of multivariate location and shape, defilllng S-estimators usin...
Consider a p-dimensional sample X = (x1,..., xn) of size n. In this paper we will concentrate on Sta...
A powerful procedure for outlier detection and robust estimation of shape and location with multivar...
This article derives the influence function of the Stahel-Donoho estimator of multivariate location ...
The Stahel-Donoho estimator is defined as a weighted mean and covariance, where each observation rec...
The authors are to be commended for bringing the critical problem of cellwise outliers to the attent...
AbstractThis article proposes a reweighted estimator of multivariate location and scatter, with weig...
AbstractIn this article, we study a class of projection based scatter depth functions proposed by Zu...
All known robust location and scale estimators with high breakdown point for multivariate samples ar...
All known robust location and scale estimators with high breakdown point for multivariate sample&apo...