Orthogonalization of multivariate location estimators: the orthomedian

Multivariate Monotone Location Estimators

A notion of monotonicity of multivariate location estimators is introduced. The relationships between this notion and other existing notions are discussed. Finite sample breakdown point properties of multivariate monotone location estimators are investigated. It turns out that the breakdown points of typical multivariate monotone location estimators are independent of the configuration of under...

Multivariate Location: Robust Estimators And Inference

The DetS and DetMM estimators for multivariate location and scatter

New deterministic robust estimators of multivariate location and scatter are presented. They combine ideas from the deterministic DetMCD estimator with steps from the subsampling-based FastS and FastMM algorithms. The new DetS and DetMM estimators perform similarly to FastS and FastMM on low-dimensional data, whereas in high dimensions they are more robust. Their computation time is much lower ...

Multivariate Location Estimators with the Best Possible Finite Sample Breakdown

The sample mean has long been used as an estimator of a location pa rameter in statistical data analysis and inference Though attractive from many viewpoints it su ers from an extreme sensitivity to outliers The median has been adopted as a more robust location estimator in one dimension it will not break down even if up to half of the data points are bad Another desirable property of the media...

Robust Location and Scatter Estimators in Multivariate Analysis

The sample mean vector and the sample covariance matrix are the corner stone of the classical multivariate analysis. They are optimal when the underlying data are normal. They, however, are notorious for being extremely sensitive to outliers and heavy tailed noise data. This article surveys robust alternatives of these classical location and scatter estimators and discusses their applications t...