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Article

Keywords:
data depth; nonparametric multivariate analysis; strong consistency of depth; mixture of distributions
Summary:
Generalised halfspace depth function is proposed. Basic properties of this depth function including the strong consistency are studied. We show, on several examples that our depth function may be considered to be more appropriate for nonsymetric distributions or for mixtures of distributions.
References:
[1] A. DasGupta, J. K. Ghosh, and M. M. Zen: A new general method for constructing confidence sets in arbitrary dimensions with applications. Ann. Statist. 23 (1995), 1408–1432. MR 1353512
[2] D. Donoho and M. Gasko: Breakdown properties of location estimates based on halfspace depth and projected outlyingness. Ann. Statist. 20 (1992), 1803–1827. MR 1193313
[3] R. Y. Liu: On a notion of data depth based on random simplices. Ann. Statist. 18 (1990), 405–414. MR 1041400 | Zbl 0701.62063
[4] R. Y. Liu, R. Serfling, and D. L. Souvaine (eds.): DIMACS; Data Depth: Robust Multivariate Analysis, Computational Geometry and Applications. American Mathematical Society, Providence RI 2006. MR 2343124
[5] J. Matoušek: Computing the center of planar point sets. In: DIMACS; Discrete and Computational Geometry (J. E. Goodman, R. Pollack, and W. Steiger, eds.). American Mathematical Society, 1992. MR 1143299
[6] I. Mizera: On depth and deep points: a calculus. Ann. Statist. 30 (2002), 1681–1736. MR 1969447 | Zbl 1039.62046
[7] P. Rousseeuw and I. Ruts: Algorithm AS307: bivariate location depth. J. Roy. Statist. Soc.-C 45 (1996), 516–526.
[8] J. Tukey: Mathematics and picturing data. In: Proc. 1975 International Congress of Mathematics, Vol. 2 (1975), pp. 523–531. MR 0426989
[9] S. van de Geer: Empirical Processes and M-Estimates. Cambridge, 2000.
[10] Y. Zuo and R. Serfling: General notion of statistical depth function. Ann. Statist. 28 (2000), 461–482. MR 1790005
[11] Y. Zuo and R. Serfling: Structural properties and convergence results for contours of sample statistical depth functions. Ann. Statist. 28 (2000), 483–499. MR 1790006
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