Concept of Data Depth and Its Applications

Ondřej Vencálek

Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica (2011)

  • Volume: 50, Issue: 2, page 111-119
  • ISSN: 0231-9721

Abstract

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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.

How to cite

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Vencálek, Ondřej. "Concept of Data Depth and Its Applications." Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica 50.2 (2011): 111-119. <http://eudml.org/doc/196472>.

@article{Vencálek2011,
abstract = {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.},
author = {Vencálek, Ondřej},
journal = {Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica},
keywords = {data depth; nonparametric multivariate analysis; applications; rank; data depth; nonparametric multivariate analysis; applications; rank},
language = {eng},
number = {2},
pages = {111-119},
publisher = {Palacký University Olomouc},
title = {Concept of Data Depth and Its Applications},
url = {http://eudml.org/doc/196472},
volume = {50},
year = {2011},
}

TY - JOUR
AU - Vencálek, Ondřej
TI - Concept of Data Depth and Its Applications
JO - Acta Universitatis Palackianae Olomucensis. Facultas Rerum Naturalium. Mathematica
PY - 2011
PB - Palacký University Olomouc
VL - 50
IS - 2
SP - 111
EP - 119
AB - 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.
LA - eng
KW - data depth; nonparametric multivariate analysis; applications; rank; data depth; nonparametric multivariate analysis; applications; rank
UR - http://eudml.org/doc/196472
ER -

References

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  4. Liu, R. Y., Singh, K., Rank tests for multivariate scale difference based on data depth, In: Liu, R. Y., Serfling, R., Souvaine, D. L. (eds.) DIMACS; Robust Multivariate Analysis, Computational Geometry and Applications American Mathematical Society, 2006, 17–34. (2006) MR2343110
  5. Liu, R. Y., Parelius, J. M., Singh, K., 10.1214/aos/1018031259, Annals of Statistics 27 (1999), 783–858. (1999) MR1724033DOI10.1214/aos/1018031259
  6. Rousseeuw, P. J., Ruts, I., Tukey, J., The bagplot: a bivariate boxplot, The American Statistician 53 (1999), 382–387. (1999) 
  7. Tukey, J., Mathematics and picturing data, Proceedings of the 1975 International Congress of Mathematics 2 (1975), 523–531. (1975) MR0426989
  8. Zuo, Y., Serfling, R., 10.1214/aos/1016218226, Annals of Statistics 28 (2000), 461–482. (2000) MR1790005DOI10.1214/aos/1016218226

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