Robust adjusted discriminant analysis based on shrinkage with application to geochemical and environmental fields Articles uri icon

publication date

  • February 2022

volume

  • 221

International Standard Serial Number (ISSN)

  • 0169-7439

Electronic International Standard Serial Number (EISSN)

  • 1873-3239

abstract

  • (copyright) 2021 Elsevier B.V.A novel discriminant analysis (DA) method is proposed, based on the robust reweighted shrinkage estimators and a robust Mahalanobis distance with an adjusted quantile as threshold. A simulation study is done to evaluate the performance of the proposed approach in comparison with the classical DA and the other robust alternatives from the literature. The approach is also illustrated using real dataset examples: a geochemical and environmental dataset known as the Kola Project and a second data containing the spectra of different cultivars of a fruit. The results show the appropriateness of the method while being computationally efficient at the same time. Additional simulations are included to show the additional benefits in outlier detection.

subjects

  • Statistics

keywords

  • adjusted quantile; kola project; multivariate outliers; robust discriminant analysis; shrinkage