A Stahel–Donoho estimator based on huberized outlyingness

Aelst, Stefan Van; Vandervieren, Ellen; Willems, Gert

doi:10.1016/j.csda.2011.08.014

Cited by 45 publications

(26 citation statements)

References 24 publications

(31 reference statements)

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“…outlying measurements may exist in such a way that the majority of observations are contaminated in at least one of their components. Van Aelst et al (2012) adapt the Stahel-Donoho estimator by huberizing the data before the outlyingness is computed. They show that their proposal could better withstand large numbers of outliers.…”

Section: Final Remarksmentioning

confidence: 99%

Discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample” by Andrea Cerioli, Marco Riani, Anthony C. Atkinson and Aldo Corbellini

Héritier

Victoria‐Feser

2017

Stat Methods Appl

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This paper discusses the contribution of Cerioli et al. (Stat Methods Appl, 2018), where robust monitoring based on high breakdown point estimators is proposed for multivariate data. The results follow years of development in robust diagnostic techniques. We discuss the issues of extending data monitoring to other models with complex structure, e.g. factor analysis, mixed linear models for which S and M Mestimators exist or deviating data cells. We emphasise the importance of robust testing that is often overlooked despite robust tests being readily available once S and M Mestimators have been defined. We mention open questions like out-of-sample inference or big data issues that would benefit from monitoring.

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Section: Final Remarksmentioning

confidence: 99%

Discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample” by Andrea Cerioli, Marco Riani, Anthony C. Atkinson and Aldo Corbellini

Héritier

Victoria‐Feser

2017

Stat Methods Appl

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“…A set of 109 representative samples of the PDO Jianning-WLS were collected from the original producing area (Jianning, Fujian) and 120 non-PDO WLS objects were collected from five main producing areas, namely, Jiangsu (26), Hunan (25), Hubei (27), Jiangxi (20), and Zhejiang (22). All the samples were harvested in 2013 and were kept with intact packaging in a cool and dark place before NIR analysis.…”

Section: Collection Of Samplesmentioning

confidence: 99%

“…Outliers can be caused by many factors and it is not trivial to detect high-dimensional outliers. Considering the high-dimensional nature of NIR spectra and the possible masking effects caused by the coexistence of multioutliers, the robust Stahel-Donoho estimate (SDE) of outlyingness [25] was adopted for outlier detection for the PDO and non-PDO WLS samples. This method projects each high-dimensional data point onto randomly generated unit vectors for many times (e.g., 500 or 1000).…”

Section: Preprocessing Outlier Diagnosis and Data Splittingmentioning

confidence: 99%

Discriminating the Geographical Origins of Chinese White Lotus Seeds by Near-Infrared Spectroscopy and Chemometrics

Cai

She

et al. 2015

Journal of Spectroscopy

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The traceability of a Chinese white lotus seed (WLS) with Protected Designation of Origin (PDO) was investigated using nearinfrared (NIR) spectroscopy and chemometrics. Three chemometrics methods, discrimination analysis (DA), class modeling, and a newly proposed strategy, the fusion of DA and class modeling, were investigated to compare their capacity to trace the geographical origins of WLS. Least squares support vector machine (LS-SVM) was developed to distinguish the PDO WLS from non-PDO WLS of four main producing areas. A class modeling technique, one-class partial least squares (OCPLS), was developed only using the data of PDO WLS. By the fusion of LS-SVM and OCPLS, the best prediction sensitivity and specificity were 0.900 and 0.973, respectively. The results indicate that fusion of DA and class modeling can enhance the specificity for detection of non-PDO products. The conclusion is that DA and class modeling should be combined for tracing food geographical origins.

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“…For principal component analysis, Van Aelst et al [2010] develop a method based on pairwise correlation that can deal with cellwise contamination. The same authors propose versions of the Stahel-Donoho estimator based on Huberized outlyingness [see Van Aelst et al, 2012] and cellwise weights [see Van Aelst et al, 2011].…”

Section: Introductionmentioning

confidence: 99%

The shooting S-estimator for robust regression

2015

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To perform multiple regression, the least squares estimator is commonly used. However, this estimator is not robust to outliers. Therefore, robust methods such as S-estimation have been proposed. These estimators flag any observation with a large residual as an outlier and downweight it in the further procedure. However, a large residual may be caused by an outlier in only one single predictor variable, and downweighting the complete observation results in a loss of information. Therefore, we propose the shooting S-estimator, a regression estimator that is especially designed for situations where a large number of observations suffer from contamination in a small number of predictor variables. The shooting S-estimator combines the ideas of the coordinate descent algorithm with simple S-regression, which makes it robust against componentwise contamination, at the cost of failing the regression equivariance property.

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A Stahel–Donoho estimator based on huberized outlyingness

Cited by 45 publications

References 24 publications

Discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample” by Andrea Cerioli, Marco Riani, Anthony C. Atkinson and Aldo Corbellini

Discussion of “The power of monitoring: how to make the most of a contaminated multivariate sample” by Andrea Cerioli, Marco Riani, Anthony C. Atkinson and Aldo Corbellini

Discriminating the Geographical Origins of Chinese White Lotus Seeds by Near-Infrared Spectroscopy and Chemometrics

The shooting S-estimator for robust regression

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