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Permutation tests using least distance estimator in the multivariate regression model

Authors
Sohn, SooncheolJung, Byoung CheolJhun, Myoungshic
Issue Date
6월-2012
Publisher
SPRINGER HEIDELBERG
Keywords
Least distance estimator; Least squares estimator; Permutation test; Bootstrap estimator
Citation
COMPUTATIONAL STATISTICS, v.27, no.2, pp.191 - 201
Indexed
SCIE
SCOPUS
Journal Title
COMPUTATIONAL STATISTICS
Volume
27
Number
2
Start Page
191
End Page
201
URI
https://scholar.korea.ac.kr/handle/2021.sw.korea/108359
DOI
10.1007/s00180-011-0247-3
ISSN
0943-4062
Abstract
This paper proposes two permutation tests based on the least distance estimator in a multivariate regression model. One is a type of t test statistic using the bootstrap method, and the other is a type of F test statistic using the sum of distances between observed and predicted values under the full and reduced models. We conducted a simulation study to compare the power of the proposed permutation tests with that of the parametric tests based on the least squares estimator for three types of hypotheses in several error distributions. The results indicate that the power of the proposed permutation tests is greater than that of the parametric tests when the error distribution is skewed like the Wishart distribution, has a heavy tail like the Cauchy distribution, or has outliers.
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