Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Conference Paper Faculty Publications- Conference Papers Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Gowri, M.S.; Kalaivani, S.; Prasad, Smrity Title A name given to the resource Robust Statistical Depth Methods for Medical Data: A Focus on Location Estimation and Classification Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2026 Source A related resource from which the described resource is derived Communications in Computer and Information Science;Volume;2853 CCIS;pp.233-244 Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.1007/978-981-95-7292-2_18" target="_blank" rel="noreferrer noopener">https://doi.org/10.1007/978-981-95-7292-2_18</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105038749178?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105038749178?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Gowri M.S., Department of Data Science and Statistics, CHRIST University, Bangalore, India; Kalaivani S., Department of Data Science and Statistics, CHRIST University, Bangalore, India; Prasad S., Department of Data Science and Statistics, CHRIST University, Bangalore, India Description An account of the resource In robust statistics, data depth functions are extremely powerful and can provide measures of central tendency beyond the ordinary means and medians. These functions provide a sense of depth to points in multivariate space, providing by default a center-outward ranking of observations, which is resistant to outliers and which can be applied to complex and high-dimensional data. Various data depth processes are considered to determine the most optimal location measure with real and simulated data. The performance of Mahalanobis Depth (MD), Half-space Depth (HSD), Zonoid Depth (ZD), Projection Depth (PD), and Spatial Depth (SPD) are compared on some health datasets including the Pima Indians Diabetes Dataset and the Wisconsin Breast Cancer (WBCD) Dataset. The results of these procedures are studied based on calculated depth values and error rates in the discriminant analysis. The findings suggest that the highest depth values are always exhibited by Spatial Depth (SPD), with better robustness and stability without losing accuracy, thus making it the best option. Nevertheless, Mahalanobis Depth (MD) also performs well, which is why it is highly applicable to the robust statistical modelling. Moreover, a new Generalized Mahalanobis Depth (GMD) has been proposed, based on robust location and scatter estimators to eliminate the weaknesses of classical MD. The GMD is more robust to contamination and is valid with singular or ill-conditioned covariance structures, and to high-dimensional data of relevance to real-world data, achieving lower misclassification rates. The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2026. Subject The topic of the resource Data Depth Functions; Generalized Mahalanobis Depth; Robust Statistics Publisher An entity responsible for making the resource available Springer Science and Business Media Deutschland GmbH Relation A related resource ISSN: 18650929; ISBN: 978-981957291-5; Language A language of the resource English Type The nature or genre of the resource Conference paper Rights Information about rights held in and over the resource Restricted Access; Hardcopy may be available in the library Format The file format, physical medium, or dimensions of the resource online