References of "Croux, C"
     in
Bookmark and Share    
Full Text
Peer Reviewed
See detailDepression and socio-econornic risk factors: 7-year longitudinal population study
Lorant, V.; Croux, C.; Weich, S. et al

in British Journal of Psychiatry (2007), 190

Background Low socio-economic status is associated with a higher prevalence of depression, but it is not yet known whether change in socio-economic status leads to a change in rates of depression. Aims To ... [more ▼]

Background Low socio-economic status is associated with a higher prevalence of depression, but it is not yet known whether change in socio-economic status leads to a change in rates of depression. Aims To assess whether longitudinal change in socio-economic factors affects change of depression level. Method In a prospective cohort study using the annual Belgian Household Panel Survey (1992-1999), depression was assessed using the Global Depression Scale. Socio-economic factors were assessed with regard to material standard of living, education, employment status and social relationships. Results A lowering in material standard of living between annual waves was associated with increases in depressive symptoms and caseness of major depression. Life circumstances also influenced depression. Ceasing to cohabit with a partner increased depressive symptoms and caseness, and improvement in circumstances reduced them; the negative effects were stronger than the positive ones. Conclusions The study showed a clear relationship between worsening socioeconomic circumstances and depression. [less ▲]

Detailed reference viewed: 39 (9 ULg)
Full Text
Peer Reviewed
See detailImplementing the Bianco and Yohai estimator for logistic regression
Croux, C.; Haesbroeck, Gentiane ULg

in Computational Statistics & Data Analysis (2003), 44(1-2), 273-295

A fast and stable algorithm to compute a highly robust estimator for the logistic regression model is proposed. A criterium. for the existence of this estimator at finite samples is derived and the ... [more ▼]

A fast and stable algorithm to compute a highly robust estimator for the logistic regression model is proposed. A criterium. for the existence of this estimator at finite samples is derived and the problem of the selection of an appropriate loss function is discussed. It is shown that the loss function can be chosen such that the robust estimator exists if and only if the maximum likelihood estimator exists. The advantages of using a weighted version of this estimator are also considered. Simulations and an example give further support for the good performance of the implemented estimators. (C) 2003 Elsevier B.V. All rights reserved. [less ▲]

Detailed reference viewed: 29 (4 ULg)
Full Text
Peer Reviewed
See detailThe breakdown behavior of the maximum likelihood estimator in the logistic regression model
Croux, C.; Flandre, C.; Haesbroeck, Gentiane ULg

in Statistics & Probability Letters (2002), 60(4), 377-386

In this note we discuss the breakdown behavior of the maximum likelihood (ML) estimator in the logistic regression model. We formally prove that the ML-estimator never explodes to infinity, but rather ... [more ▼]

In this note we discuss the breakdown behavior of the maximum likelihood (ML) estimator in the logistic regression model. We formally prove that the ML-estimator never explodes to infinity, but rather breaks down to zero when adding severe outliers to a data set. An example confirms this behavior. (C) 2002 Published by Elsevier Science B.V. [less ▲]

Detailed reference viewed: 19 (3 ULg)
Full Text
Peer Reviewed
See detailLocation adjustment for the minimum volume ellipsoid estimator
Croux, C.; Haesbroeck, Gentiane ULg; Rousseeuw, P. J.

in Statistics and Computing (2002), 12(3), 191-200

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 Minimum Volume ... [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 Minimum Volume Ellipsoid Estimator (MVE). Computing the exact MVE is often not feasible, so one usually resorts to an approximate algorithm. In the regression setup, algorithms for positive-breakdown estimators like Least Median of Squares typically recompute the intercept at each step, to improve the result. This approach is called intercept adjustment. In this paper we show that a similar technique, called location adjustment, can be applied to the MVE. For this purpose we use the Minimum Volume Ball (MVB), in order to lower the MVE objective function. An exact algorithm for calculating the MVB is presented. As an alternative to MVB location adjustment we propose L-1 location adjustment, which does not necessarily lower the MVE objective function but yields more efficient estimates for the location part. Simulations compare the two types of location adjustment. We also obtain the maxbias curves of both L-1 and the MVB in the multivariate setting, revealing the superiority of L-1. [less ▲]

Detailed reference viewed: 24 (1 ULg)
Full Text
Peer Reviewed
See detailMaxbias curves of robust location estimators based on subranges
Croux, C.; Haesbroeck, Gentiane ULg

in Journal of Nonparametric Statistics (2002), 14(3), 295-306

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 are ... [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 are computed for some univariate location estimators based on subranges: midranges, trimmed means and the univariate Minimum Volume Ellipsoid (MVE) location estimators. These estimators are intuitively appealing and easy to calculate. [less ▲]

Detailed reference viewed: 14 (3 ULg)
Full Text
Peer Reviewed
See detailA note on finite-sample efficiencies of estimators for the minimum volume ellipsoid
Croux, C.; Haesbroeck, Gentiane ULg

in Journal of Statistical Computation & Simulation (2002), 72(7), 585-596

Among the most well known estimators of multivariate location and scatter is the Minimum Volume Ellipsoid (MVE). Many algorithms have been proposed to compute it. Most of these attempt merely to ... [more ▼]

Among the most well known estimators of multivariate location and scatter is the Minimum Volume Ellipsoid (MVE). Many algorithms have been proposed to compute it. Most of these attempt merely to approximate as close as possible the exact MVE, but some of them led to the definition of new estimators which maintain the properties of robustness and affine equivariance that make the MVE so attractive. Rousseeuw and van Zomeren (1990) used the (p+1)- subset estimator which was modified by Croux and Haesbroeck (1997) to give rise to the averaged (p+1)- subset estimator . This note shows by means of simulations that the averaged (p+1)-subset estimator outperforms the exact estimator as far as finite-sample efficiency is concerned. We also present a new robust estimator for the MVE, closely related to the averaged (p+1)-subset estimator, but yielding a natural ranking of the data. [less ▲]

Detailed reference viewed: 20 (4 ULg)
Full Text
Peer Reviewed
See detailMaxbias curves of robust scale estimators based on subranges
Croux, C.; Haesbroeck, Gentiane ULg

in Metrika (2001), 53(2), 101-122

A maxbias curve is a powerful tool to describe the robustness of an estimator. It is an asymptotic concept which tells how much an estimator can change due to a given fraction of contamination. In this ... [more ▼]

A maxbias curve is a powerful tool to describe the robustness of an estimator. It is an asymptotic concept which tells how much an estimator can change due to a given fraction of contamination. In this paper, maxbias curves are computed for some univariate scale estimators based on subranges: trimmed standard deviations, interquantile ranges and the univariate Minimum Volume Ellipsoid (MVE) and Minimum Covariance Determinant (MCD) scale estimators. These estimators are intuitively appealing and easy to calculate. Since the bias behavior of scale estimators may differ depending on the type of contamination (outliers or inliers), expressions for both explosion and implosion maxbias curves are given. On the basis of robustness and efficiency arguments, the MCD scale estimator with 25% breakdown point can be recommended for practical use. [less ▲]

Detailed reference viewed: 10 (0 ULg)
Full Text
Peer Reviewed
See detailPrincipal component analysis based on robust estimators of the covariance or correlation matrix: Influence functions and efficiencies
Croux, C.; Haesbroeck, Gentiane ULg

in Biometrika (2000), 87(3), 603-618

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 the ... [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 the influence functions and the corresponding asymptotic variances for these robust estimators of eigenvalues and eigenvectors. The behaviour of several of these estimators is investigated by a simulation study. It turns out that the theoretical results and simulations favour the use of S-estimators, since they combine a high efficiency with appealing robustness properties. [less ▲]

Detailed reference viewed: 23 (6 ULg)
Full Text
Peer Reviewed
See detailInfluence function and efficiency of the minimum covariance determinant scatter matrix estimator
Croux, C.; Haesbroeck, Gentiane ULg

in Journal of Multivariate Analysis (1999), 71(2), 161-190

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 to compute ... [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 to compute and intuitively appealing. In this note we derive its influence function and compute the asymptotic variances of its elements. A comparison with the one step reweighted MCD and with S-estimators is made. Also finite-sample results are reported. (C) 1999 Academic Press AMS 1991 subject classifications: 62F35, 62G35. [less ▲]

Detailed reference viewed: 31 (5 ULg)