Geodesic
Concept of Data Depth and Its Applications
Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica, Tome 50 (2011) no. 2, pp. 111-119 Cet article a éte moissonné depuis la source Czech Digital Mathematics Library

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Data depth is an important concept of nonparametric approach to multivariate data analysis. The main aim of the paper is to review possible applications of the data depth, including outlier detection, robust and affine-equivariant estimates of location, rank tests for multivariate scale difference, control charts for multivariate processes, and depth-based classifiers solving discrimination problem.
Data depth is an important concept of nonparametric approach to multivariate data analysis. The main aim of the paper is to review possible applications of the data depth, including outlier detection, robust and affine-equivariant estimates of location, rank tests for multivariate scale difference, control charts for multivariate processes, and depth-based classifiers solving discrimination problem.
Classification : 60D05, 62G05, 62G15, 62H05
Keywords: data depth; nonparametric multivariate analysis; applications; rank 
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     title = {Concept of {Data} {Depth} and {Its} {Applications}},
     journal = {Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica},
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Vencálek, Ondřej. Concept of Data Depth and Its Applications. Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica, Tome 50 (2011) no. 2, pp. 111-119. http://geodesic.mathdoc.fr/item/AUPO_2011_50_2_a11/