Robust weighted orthogonal regression in the errors-in-variables model

Fekri, M.; Ruiz‐Gazen, Anne

doi:10.1016/s0047-259x(03)00057-5

Cited by 33 publications

(34 citation statements)

References 25 publications

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“…Cheng and Van Ness (1992) generalized this proposal. Fekri and Ruiz-Gazen (2004) used eigenvectors of robust estimators of multivariate scatter of Z 1 , . .…”

Section: Introductionmentioning

confidence: 99%

Fast and robust estimation of the multivariate errors in variables model

Croux¹,

Fekri

Ruiz‐Gazen

2009

TEST

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“…Cheng and Van Ness (1992) generalized this proposal. Fekri and Ruiz-Gazen (2004) used eigenvectors of robust estimators of multivariate scatter of Z 1 , . .…”

Section: Introductionmentioning

confidence: 99%

Fast and robust estimation of the multivariate errors in variables model

Croux¹,

Fekri

Ruiz‐Gazen

2009

TEST

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“…Hubert and Branden [13] introduced robustified versions of the SIMPLS algorithm. A robust multivariate calibration model was used by Hubert and Verboven [19], and a robust error in variable regression was used by Fekri and Ruiz-Gazen [9]. The MCD algorithm was used for a genetic algorithm by Wiegand et al [29]).…”

Section: Introductionmentioning

confidence: 99%

Performance of Linear Discriminant Analysis Using Different Robust Methods

Alrawashdeh¹,

Radwan²,

Abunawas³

2018

Eur. J. Pure Appl. Math.

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Abstract. This study aims to combine the new deterministic minimum covariance determinant (DetMCD) algorithm with linear discriminant analysis (LDA) and compare it with the fast minimum covariance determinant (FastMCD), fast consistent high breakdown (FCH), and robust FCH (RFCH) algorithms. LDA classifies new observations into one of the unknown groups and it is widely used in multivariate statistical analysis. The LDA mean and covariance matrix parameters are highly influenced by outliers. The DetMCD algorithm is highly robust and resistant to outliers and it is constructed to overcome the outlier problem. Moreover, the DetMCD algorithm is used to estimate location and scatter matrices. The DetMCD, FastMCD, FCH, and RFCH algorithms are applied to estimate misclassification probability using robust LDA. All the algorithms are expected to improve the LDA model for classification purposes in banks, such as bankruptcy and failures, and to distinguish between Islamic and conventional banks. The performances of the estimators are investigated through simulation and actual data..

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“…So robust approaches are needed. In Fekri and Ruiz-Gazen (2004), we consider the (H 1 ) hypothesis and define a new class of robust estimators through a weighted least-squares criterion. These estimators, called robust weighted orthogonal regression estimators, are easily derived from robust multivariate location and scatter estimators from which they inherit the B-robustness property (bounded influence function).…”

Section: Introductionmentioning

confidence: 99%