Nonparametric Tests Applicable to High Dimensional Data | Zendy

František Rublík | Zendy

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Nonparametric Tests Applicable to High Dimensional Data

Author(s) -

František Rublík

Publication year - 2019

Publication title -

austrian journal of statistics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 0.342

H-Index - 9

ISSN - 1026-597X

DOI - 10.17713/ajs.v48i4.654

Subject(s) - nonparametric statistics , multivariate statistics , dimension (graph theory) , statistics , set (abstract data type) , sample (material) , data mining , mathematics , data set , sample size determination , computer science , chemistry , chromatography , programming language , pure mathematics

Constructions of data driven ordering of set of multivariate observations are presented. The methods employ also dissimilarity measures. The ranks are used in the construction of test statistics for location problem and in the construction of the corresponding multiple comparisons rule. An important aspect of the resulting procedures is that they can be used also in the multisample setting and in situations where the sample size is smaller than the dimension of the observations. The performance of the proposed procedures is illustrated by simulations.

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