A1 Vertaisarvioitu alkuperäisartikkeli tieteellisessä lehdessä
A weighted multivariate signed-rank test for cluster-correlated data
Tekijät: Haataja R, Larocque D, Nevalainen J, Oja H
Kustantaja: ELSEVIER INC
Julkaisuvuosi: 2009
Lehti:: Journal of Multivariate Analysis
Tietokannassa oleva lehden nimi: JOURNAL OF MULTIVARIATE ANALYSIS
Lehden akronyymi: J MULTIVARIATE ANAL
Vuosikerta: 100
Numero: 6
Aloitussivu: 1107
Lopetussivu: 1119
Sivujen määrä: 13
ISSN: 0047-259X
DOI: https://doi.org/10.1016/j.jmva.2008.10.009
Tiivistelmä
A weighted multivariate signed-rank test is introduced for an analysis of multivariate clustered data. Observations in different clusters may then get different weights. The test provides a robust and efficient alternative to normal theory based methods. Asymptotic theory is developed to find the approximate p-value as well as to calculate the limiting Pitman efficiency of the test. A conditionally distribution-free version of the test is also discussed. The finite-sample behavior of different versions of the test statistic is explored by simulations and the new test is compared to the unweighted and weighted versions of Hotelling's T(2) test and the multivariate spatial sign test introduced in [ D. Larocque, J. Nevalainen, H. Oja, A weighted multivariate sign test for cluster-correlated data, Biometrika 94 (2007) 267-283]. Finally, a real data example is used to illustrate the theory. (C) 2008 Elsevier Inc. All rights reserved.
A weighted multivariate signed-rank test is introduced for an analysis of multivariate clustered data. Observations in different clusters may then get different weights. The test provides a robust and efficient alternative to normal theory based methods. Asymptotic theory is developed to find the approximate p-value as well as to calculate the limiting Pitman efficiency of the test. A conditionally distribution-free version of the test is also discussed. The finite-sample behavior of different versions of the test statistic is explored by simulations and the new test is compared to the unweighted and weighted versions of Hotelling's T(2) test and the multivariate spatial sign test introduced in [ D. Larocque, J. Nevalainen, H. Oja, A weighted multivariate sign test for cluster-correlated data, Biometrika 94 (2007) 267-283]. Finally, a real data example is used to illustrate the theory. (C) 2008 Elsevier Inc. All rights reserved.
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