Measure of Location using Data Depth Procedures

R.Muthukrishnan ¹ , D.Gowri ² , N.Ramkumar ³

Section:Research Paper, Product Type: Isroset-Journal
Vol.5 , Issue.6 , pp.273-277, Dec-2018

CrossRef-DOI:   https://doi.org/10.26438/ijsrmss/v5i6.273277

Online published on Dec 31, 2018

Copyright © R.Muthukrishnan, D.Gowri, N.Ramkumar . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
 

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Abstract :
Data depth is used to measure the depth or outlyingness of a given multivariate sample with respect to its underlying distribution. It can be lead to a natural center-outward ordering of sample points. The essence of depth function in multivariate analyses is to measure the degree of centrality of point relative to a data set or probability distribution. This work explores data depth procedures in order to find the measure of location, namely deepest or center point. Further, the various depth procedures are examined under real and simulation environment with the help of R software. The efficiency of various data depth procedures have been studied by computing average misclassification error in the context of discriminant analysis with numerical illustration

Key-Words / Index Term :
Data Depth, Location and Linear discriminant analysis

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