In 1996, Bianco and Yohai proposed a highly robust estimation procedure in the logistic regression model. The theoretical results they obtain were very promising. In this paper we complement theirâ€¦ (More)

A robust principal component analysis can be easily performed by computing the eigenvalues and eigenvectors of a robust estimator of the covariance or correlation matrix. In this paper we derive theâ€¦ (More)

The Minimum Covariance Determinant (MCD) estimator is a highly robust procedure for estimating the centre and shape of a high dimensional data set. It consists of determining a subsample of h pointsâ€¦ (More)

A maxbias curve is a powerful tool to describe the robustness of an estimator. It tells us how much an estimator can change due to a given fraction of contamination. In this paper, maxbias curves areâ€¦ (More)

Logistic regression is frequently used for classifying observations into two groups. Unfortunately there are often outlying observations in a data set, who might affect the estimated model and theâ€¦ (More)

We give subtle, simple and precise results about the convergence or the divergence of the sequence (xn), where xj = f(xjâˆ’1) for every integer j, when the initial element x0 is in the neighbourhood ofâ€¦ (More)

The Minimum Covariance Determinant (MCD) scatter estimator is a highly robust estimator for the dispersion matrix of a multivariate, elliptically symmetric distribution. It is relatively fast toâ€¦ (More)

Estimating multivariate location and scatter with both affine equivariance and positive breakdown has always been difficult. A well-known estimator which satisfies both properties is the Minimumâ€¦ (More)

Outlier detection techniques in spatial data should allow to identify two types of outliers: global and local ones. Local outliers typically have non-spatial attributes that strongly differ fromâ€¦ (More)