Robust estimation of multivariate location and shape

Multivariate Location: Robust Estimators And Inference

Robust Estimation of Multivariate Location and Scatter based on the Effective Independence Distribution

This paper applies a method known in the engineering literature as Effective Independence Distribution (EID) to an approximation of the minimum volume ellipsoid, a high breakdown robust estimator of multivariate location and scale. The calculation of the minimum volume ellipsoid is an extremely computationally intensive task. Previous methods for approximating the MVE have focused on bootstrap-...

Robust estimation of shape parameters

We investigate the use of Robust Estimation in an application requiring the accurate location of the centres of circular objects in an image. A common approach used throughout computer vision for extracting shape information from a data set is to fit a feature model using the Least Squares method. The well known sensitivity of this method to outliers is traditionally accommodated by outlier rej...

A Robust Estimation of Location and Scatter

Statisticians face increasingly the task of analyzing large and high dimension multivariate data sets. This is due to the advances in computer technology which have facilitated greatly the collection of large data sets and, on the other hand, to the fact that most statistical experiments are multivariate in nature. One of the primary problems encountered in this task is robust estimation of loc...

An L1-type estimator of multivariate location and shape

The proposed estimator is a location and shape estimator which generalizes the L1-idea to a multivariate context. Consider a sample x1, . . . , xn of p-variate observations. Then the estimator is defined as the solution (μ̂, V̂ ) that yields the minimum of the sum of the distances di(μ, V ) = √ (xi − μ)′V −1(xi − μ), minimized under the constraint that V has determinant 1. The constraint det(V ) ...