Examinando por Materia "Covarianza"

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    Detecting Outliers with a Non-parametric estimation of the Mahalanobis distance
    (Universidad EAFIT, 2023) Piedrahita Jaramillo, Catalina; Laniado Rodas, Henry; Saldarriaga Aristizábal, Pablo Andrés
    This paper proposes the creation of a robust version of the Mahalanobis distance for the outlier’s identification problem, using robust and non-parametric estimations for the covariance matrix, such as Kendall’s Tau and Median Absolute Deviation (MAD), as well as techniques that enhance the numerical properties of the covariance matrix to reduce error during numerical calculations like Ledoit and Wolf’s Shrinkage. The performance of the methods is evaluated through simulation of independent normal data, correlated normal data, and real data sets and compared with some methods from the literature. The proposed methods achieve a high percentage of correct identification of outliers and have a low false positive rate for both data types, particularly in the case of correlated normal data.