Permutation tests for equality of distributions in high-dimensional settings
Hall, P and Tajvidi, Nader LU
(2002)
In Biometrika
89(2).
p.359-374
- Abstract
- Motivated by applications in high-dimensional settings, we suggest a test of the hypothesis H-0 that two sampled distributions are identical. It is assumed that two independent datasets are drawn from the respective populations, which may be very general. In particular, the distributions may be multivariate or infinite-dimensional, in the latter case representing, for example, the distributions of random functions from one Euclidean space to another. Our test uses a measure of distance between data. This measure should be symmetric but need not satisfy the triangle inequality, so it is not essential that it be a metric. The test is based on ranking the pooled dataset, with respect to the distance and relative to any fixed data value, and... (More)
- Motivated by applications in high-dimensional settings, we suggest a test of the hypothesis H-0 that two sampled distributions are identical. It is assumed that two independent datasets are drawn from the respective populations, which may be very general. In particular, the distributions may be multivariate or infinite-dimensional, in the latter case representing, for example, the distributions of random functions from one Euclidean space to another. Our test uses a measure of distance between data. This measure should be symmetric but need not satisfy the triangle inequality, so it is not essential that it be a metric. The test is based on ranking the pooled dataset, with respect to the distance and relative to any fixed data value, and repeating this operation for each fixed datum. A permutation argument enables a critical point to be chosen such that the test has concisely known significance level, conditional on the set of all pairwise distances. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/334616
- author
- Hall, P
and Tajvidi, Nader
LU
- organization
- publishing date
- 2002
- type
- Contribution to journal
- publication status
- published
- subject
- keywords
- rank test, resampling, multivariate analysis, local alternative, hypothesis test, hypergeometric distribution, bootstrap, functional data analysis
- in
- Biometrika
- volume
- 89
- issue
- 2
- pages
- 359 - 374
- publisher
- Oxford University Press
- external identifiers
-
- wos:000176520500008
- scopus:22944460361
- ISSN
- 0006-3444
- DOI
- 10.1093/biomet/89.2.359
- language
- English
- LU publication?
- yes
- id
- 2ce5c9ac-b70a-47cd-9695-2814e5c08d6d (old id 334616)
- date added to LUP
- 2016-04-01 15:43:09
- date last changed
- 2022-03-07 01:02:41
@article{2ce5c9ac-b70a-47cd-9695-2814e5c08d6d,
abstract = {{Motivated by applications in high-dimensional settings, we suggest a test of the hypothesis H-0 that two sampled distributions are identical. It is assumed that two independent datasets are drawn from the respective populations, which may be very general. In particular, the distributions may be multivariate or infinite-dimensional, in the latter case representing, for example, the distributions of random functions from one Euclidean space to another. Our test uses a measure of distance between data. This measure should be symmetric but need not satisfy the triangle inequality, so it is not essential that it be a metric. The test is based on ranking the pooled dataset, with respect to the distance and relative to any fixed data value, and repeating this operation for each fixed datum. A permutation argument enables a critical point to be chosen such that the test has concisely known significance level, conditional on the set of all pairwise distances.}},
author = {{Hall, P and Tajvidi, Nader}},
issn = {{0006-3444}},
keywords = {{rank test; resampling; multivariate analysis; local alternative; hypothesis test; hypergeometric distribution; bootstrap; functional data analysis}},
language = {{eng}},
number = {{2}},
pages = {{359--374}},
publisher = {{Oxford University Press}},
series = {{Biometrika}},
title = {{Permutation tests for equality of distributions in high-dimensional settings}},
url = {{http://dx.doi.org/10.1093/biomet/89.2.359}},
doi = {{10.1093/biomet/89.2.359}},
volume = {{89}},
year = {{2002}},
}