Multidimensional outlier detection and robust estimation using Sn covariance

Abstract

This article presents a robust method for detecting multiple outliers from multidimensional data using robust Mahalanobis distance. Initial scatter matrix for robust Mahalanobis distance is constructed using a robust estimator of covariance (SnCov) established from a robust scale estimator Sn and casewise median are chosen to be the location vector. The performance of the proposed method is evaluated using the results of simulated samples. This outlier detection method is compared with some well-known methods available in the current literature. The application of the proposed method in real-life data is also executed in this article.