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  1. No Access

    Chapter and Conference Paper

    Robust PCA for High-dimensional Data

    Principal component analysis (PCA) is a well-known technique for dimension reduction. Classical PCA is based on the empirical mean and covariance matrix of the data, and hence is strongly affected by outlying ...

    M. Hubert, P. J. Rousseeuw, S. Verboven in Developments in Robust Statistics (2003)

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    Chapter and Conference Paper

    A Robust Hotelling Test

    Hotelling’s T2 statistic is an important tool for inference about the center of a multivariate normal population. However, hypothesis tests and confidence intervals based on this statistic can be adversely affect...

    G. Willems, G. Pison, P. J. Rousseeuw, S. Van Aelst in Developments in Robust Statistics (2003)

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    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)

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    Chapter and Conference Paper

    Some Proposals for Fast HBD Regression

    Existing high-breakdown regression estimators need substantial computation time. In this paper we propose a fast estimator for robust regression with a breakdown point of 1/3. This is not the highest value pos...

    P. J. Rousseeuw, B. C. van Zomeren in Compstat (1990)