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  1. Article

    Open Access

    The k-step spatial sign covariance matrix

    The Sign Covariance Matrix is an orthogonal equivariant estimator of multivariate scale. It is often used as an easy-to-compute and highly robust estimator. In this paper we propose a k-step version of the Sign C...

    C. Croux, C. Dehon, A. Yadine in Advances in Data Analysis and Classification (2010)

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    Article

    Robust canonical correlations: A comparative study

    Several approaches for robust canonical correlation analysis will be presented and discussed. A first method is based on the definition of canonical correlation analysis as looking for linear combinations of t...

    J. A. Branco, C. Croux, P. Filzmoser, M. R. Oliveira in Computational Statistics (2005)

  3. No Access

    Chapter and Conference Paper

    Empirical Comparison of the Classification Performance of Robust Linear and Quadratic Discriminant Analysis

    The aim of this paper is to look at the behavior of the total probability of misclassification of robust linear and quadratic discriminant analysis. The effect of outliers on the discriminant rules is studied ...

    K. Joossens, C. Croux in Theory and Applications of Recent Robust Methods (2004)

  4. No Access

    Chapter and Conference Paper

    Robust Redundancy Analysis by Alternating Regression

    Given two groups of variables redundancy analysis searches for linear combinations of variables in one group that maximize the variance of the other group that is explained by each one of the linear combinatio...

    M. R. Oliveira, J. A. Branco, C. Croux… in Theory and Applications of Recent Robust M… (2004)

  5. No Access

    Article

    Fitting multiplicative models by robust alternating regressions

    In this paper a robust approach for fitting multiplicative models is presented. Focus is on the factor analysis model, where we will estimate factor loadings and scores by a robust alternating regression algor...

    C. Croux, P. Filzmoser, G. Pison, P. J. Rousseeuw in Statistics and Computing (2003)

  6. No Access

    Chapter and Conference Paper

    Outlier resistant estimators for canonical correlation analysis

    Canonical correlation analysis studies associations between two sets of random variables. Its standard computation is based on sample covariance matrices, which are however very sensitive to outlying observati...

    P. Filzmoser, C. Dehon, C. Croux in COMPSTAT (2000)

  7. No Access

    Chapter and Conference Paper

    A robust version of principal factor analysis

    Our aim is to construct a factor analysis method that can resist the effect of outliers. We start with a highly robust initial covariance estimator, after which the factors can be obtained from maximum likelih...

    G. Pison, P. J. Rousseeuw, P. Filzmoser, C. Croux in COMPSTAT (2000)

  8. No Access

    Chapter and Conference Paper

    A Fast Algorithm for Robust Principal Components Based on Projection Pursuit

    One of the aims of a principal component analysis (PCA) is to reduce the dimensionality of a collection of observations. If we plot the first two principal components of the observations, it is often the case ...

    C. Croux, A. Ruiz-Gazen in COMPSTAT (1996)