Robust estimators for multivariate location and dispersion should be ãn consistent and highly outlier resistant, but estimators that have been shown to have these properties are impractical to compute. The RMVN estimator is an easily computed outlier resistant robust ãn consistent estimator of multivariate location and dispersion, and the estimator is obtained by scaling the classical estimator applied to the gRMVN subseth that contains at least half of the cases. Several robust estimators will be presented, discussed and compared in detail. The applications for the RMVN estimator are numerous, and a simple method for performing robust principal component analysis (PCA), canonical correlation analysis (CCA) and factor analysis is to apply t...
In this paper, we describe an overall strategy for robust estimation of multivariate location and sh...
We introduce a robust method for multivariate regression based on robust estimation of the joint loc...
In this paper we introduce weighted estimators of the location and dispersion of a multivariate data...
This text presents methods that are robust to the assumption of a multivariate normal distribution o...
A common technique for robust dispersion estimators is to apply the classical estimator to some subs...
In this paper, we propose a new componentwise estimator of a dispersion matrix, based on a highly ro...
AbstractIn this paper, we propose a new componentwise estimator of a dispersion matrix, based on a h...
Algebraically, principal components can be defined as the eigenvalues and eigenvectors of a covarian...
This paper presents a simple resistant estimator of multivariate location and dispersion. The DD plo...
This paper presents a simple resistant estimator of multivariate location and dispersion. The DD plo...
Robust estimators have been extensively developed in statistics since the pioneering work of Huber (...
Principal component analysis (PCA) is not resistant to outliers existing in multivariate data sets. ...
This paper gives concise descriptions of a robust location statistic, the remedian of P. Rousseeuw a...
We deal with the equivariant estimation of scatter and location for p-dimensional data, giving empha...
AbstractA robust principal component analysis for samples from a bivariate distribution function is ...
In this paper, we describe an overall strategy for robust estimation of multivariate location and sh...
We introduce a robust method for multivariate regression based on robust estimation of the joint loc...
In this paper we introduce weighted estimators of the location and dispersion of a multivariate data...
This text presents methods that are robust to the assumption of a multivariate normal distribution o...
A common technique for robust dispersion estimators is to apply the classical estimator to some subs...
In this paper, we propose a new componentwise estimator of a dispersion matrix, based on a highly ro...
AbstractIn this paper, we propose a new componentwise estimator of a dispersion matrix, based on a h...
Algebraically, principal components can be defined as the eigenvalues and eigenvectors of a covarian...
This paper presents a simple resistant estimator of multivariate location and dispersion. The DD plo...
This paper presents a simple resistant estimator of multivariate location and dispersion. The DD plo...
Robust estimators have been extensively developed in statistics since the pioneering work of Huber (...
Principal component analysis (PCA) is not resistant to outliers existing in multivariate data sets. ...
This paper gives concise descriptions of a robust location statistic, the remedian of P. Rousseeuw a...
We deal with the equivariant estimation of scatter and location for p-dimensional data, giving empha...
AbstractA robust principal component analysis for samples from a bivariate distribution function is ...
In this paper, we describe an overall strategy for robust estimation of multivariate location and sh...
We introduce a robust method for multivariate regression based on robust estimation of the joint loc...
In this paper we introduce weighted estimators of the location and dispersion of a multivariate data...