A weighted multivariate signed-rank test for cluster-correlated data

Riina Haataja, Denis Larocque, Jaakko Nevalainen and Hannu Oja

Journal of Multivariate Analysis, 2009, vol. 100, issue 6, 1107-1119

Abstract: 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 T2 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.

Keywords: primary; 62H15; 62G10 secondary; 62E20 Clustered observations Paired observations Intra-cluster correlation Multivariate location problem Wilcoxon signed-rank test Unweighted and weighted testing U-statistics (search for similar items in EconPapers)
Date: 2009
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Citations: View citations in EconPapers (7)

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