Publications of Stéphanie Aerts
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See detailPredictive Maintenance of Technical Faults in Aircraft
Peters, Florian ULiege; Aerts, Stéphanie ULiege; Schyns, Michael ULiege

Conference (2020, January 30)

A key issue for handlers in the air cargo industry is arrival delays due to aircraft maintenance. This work focuses on a particular delay caused by technical faults called technical delays. Using real ... [more ▼]

A key issue for handlers in the air cargo industry is arrival delays due to aircraft maintenance. This work focuses on a particular delay caused by technical faults called technical delays. Using real data from a cargo handler company, different classification models that can predict technical delay occurrence are compared. A new decision tree extension is also proposed based on a study by Hoffait & Schyns (2017). The final results present a good starting point for future research. [less ▲]

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See detailRegularized discriminant analysis : a cellwise robust approach
Aerts, Stéphanie ULiege

Conference (2018, July 03)

Quadratic and Linear Discriminant Analysis (QDA/LDA) are the most often applied classification rules under normality. In QDA, a separate covariance matrix is estimated for each group. If there are more ... [more ▼]

Quadratic and Linear Discriminant Analysis (QDA/LDA) are the most often applied classification rules under normality. In QDA, a separate covariance matrix is estimated for each group. If there are more variables than observations in the groups, the usual estimates are singular and cannot be used anymore. Assuming homoscedasticity, as in LDA, reduces the number of parameters to estimate. This rather strong assumption is however rarely verified in practice. Regularized discriminant techniques that are computable in high-dimension and cover the path between the two extremes QDA and LDA have been proposed in the literature. However, these procedures rely on sample covariance matrices. As such, they become inappropriate in presence of cellwise outliers, a type of outliers that is very likely to occur in high-dimensional datasets. In this talk, we propose cellwise robust counterparts of these regularized discriminant techniques by inserting cellwise robust covariance matrices. Our methodology results in a family of discriminant methods that (i) are robust against outlying cells, (ii) cover the gap between LDA and QDA and (iii) are computable in high-dimension. The good performance of the new methods is illustrated through simulated and real data examples. [less ▲]

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See detailRegularized Discriminant Analysis in Presence of Cellwise Contamination
Aerts, Stéphanie ULiege; Wilms, Ines

Conference (2017, August 01)

Quadratic and Linear Discriminant Analysis (QDA/LDA) are the most often applied classification rules under normality. In QDA, a separate covariance matrix is estimated for each group. If there are more ... [more ▼]

Quadratic and Linear Discriminant Analysis (QDA/LDA) are the most often applied classification rules under normality. In QDA, a separate covariance matrix is estimated for each group. If there are more variables than observations in the groups, the usual estimates are singular and cannot be used anymore. Assuming homoscedasticity, as in LDA, reduces the number of parameters to estimate. This rather strong assumption is however rarely verified in practice. Regularized discriminant techniques that are computable in high-dimension and cover the path between the two extremes QDA and LDA have been proposed in the literature. However, these procedures rely on sample covariance matrices. As such, they become inappropriate in presence of cellwise outliers, a type of outliers that is very likely to occur in high-dimensional datasets. We propose cellwise robust counterparts of these regularized discriminant techniques by inserting cellwise robust covariance matrices. Our methodology results in a family of discriminant methods that are robust against outlying cells, cover the gap between LDA and QDA and are computable in high-dimension. [less ▲]

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See detailRobust asymptotic tests for the equality of multivariate coefficients of variation
Aerts, Stéphanie ULiege; Haesbroeck, Gentiane ULiege

in TEST (2017), 26(1), 163-187

In order to easily compare several populations on the basis of more than one feature, multivariate coefficients of variation (MCV) may be used as they allow to summarize relative dispersion in a single ... [more ▼]

In order to easily compare several populations on the basis of more than one feature, multivariate coefficients of variation (MCV) may be used as they allow to summarize relative dispersion in a single index. However, up to date, no test of equality of one or more MCV's has been developed in the literature. In this paper, several classical and robust Wald type tests are proposed and studied. The asymptotic distributions of the test statistics are derived under elliptical symmetry, and the asymptotic efficiency of the robust versions is compared to the classical tests. Robustness of the proposed procedures is examined through partial influence functions of the test statistic, as well as by means of power and level influence functions. A simulation study compares the performance of the classical and robust tests under uncontaminated and contaminated schemes, and the difference with the usual covariance homogeneity test is highlighted. As a by-product, these tests may also be considered in the univariate context where they yield procedures that are both robust and easy-to-use. They provide an interesting alternative to the numerous parametric tests of comparison of univariate coefficients of variation existing in the literature, which are, in most cases, unreliable in presence of outliers. The methods are illustrated on a real data set. [less ▲]

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See detailDistribution and robustness of a distance-based multivariate coefficient of variation
Aerts, Stéphanie ULiege

Poster (2014, November)

When one wants to compare the homogeneity of a characteristic in several popula- tions that have di erent means, the advocated statistic is the univariate coe cient of variation. However, in the ... [more ▼]

When one wants to compare the homogeneity of a characteristic in several popula- tions that have di erent means, the advocated statistic is the univariate coe cient of variation. However, in the multivariate setting, comparing marginal coe cients may be inconclusive. Therefore, several extensions that summarize multivariate relative dispersion in one single in- dex have been proposed in the literature (see Albert & Zhang, 2010, for a review). In this poster, focus is on a particular extension, due to Voinov & Nikulin (1996), based on the Mahalanobis distance between the mean and the origin of the design space. Some arguments are outlined for justifying this choice. Then, properties of its sample version under elliptical symmetry are discussed. Under normality, this estimator is shown to be biased at nite samples. In order to overcome this drawback, two bias corrections are proposed. Moreover, the empirical estimator also su ers from a lack of robustness, which is illustrated by means of in uence functions. A robust counterpart based on the Minimum Covariance Determinant estimator is advocated. [less ▲]

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See detailRobustness and efficiency of multivariate coefficients of variation
Aerts, Stéphanie ULiege; Haesbroeck, Gentiane ULiege; Ruwet, Christel ULiege

Conference (2014, August 12)

The coefficient of variation is a well-known measure used in many fields to compare the variability of a variable in several populations. However, when the dimension is greater than one, comparing the ... [more ▼]

The coefficient of variation is a well-known measure used in many fields to compare the variability of a variable in several populations. However, when the dimension is greater than one, comparing the variability only marginally may lead to controversial results. Several multivariate extensions of the univariate coefficient of variation have been introduced in the literature. In practice, these coefficients can be estimated by using any pair of location and covariance estimators. However, as soon as the classical mean and covariance matrix are under consideration, the influence functions are unbounded, while the use of any robust estimators yields bounded influence functions. While useful in their own right, the influence functions of the multivariate coefficients of variation are further exploited in this talk to derive a general expression for the corresponding asymptotic variances under elliptical symmetry. Then, focusing on two of the considered multivariate coefficients, a diagnostic tool based on their influence functions is derived and compared, on a real-life dataset, with the usual distance-plot. [less ▲]

Detailed reference viewed: 79 (18 ULiège)