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On Multivariate Runs Tests for Randomness

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Author Info
Davy Paindaveine
Abstract

This paper proposes several extensions of the concept of runs to the multivariate setup, and studies the resulting tests of multivariate randomness against serial dependence. Two types of multivariate runs are defined: (i) an elliptical extension of the spherical runs proposed by Marden (1999), and (ii) an original concept of matrix-valued runs. The resulting runs tests themselves exist in various versions, one of which is a function of the number of data-based hyperplanes separating pairs of observations only. All proposed multivariate runs tests are affine-invariant and highly robust: in particular, they allow for heteroskedasticity and do not require any moment assumption. Their limiting distributions are derived under the null hypothesis and under sequences of local vector ARMA alternatives. Asymptotic relative efficiencies with respect to Gaussian Portmanteau tests are computed, and show that, while Mardentype runs tests suffer severe consistency problems, tests based on matrix-valued runs perform uniformly well for moderate-to-large dimensions. A Monte-Carlo study confirms the theoretical results and investigates the robustness properties of the proposed procedures. A real data example is also treated, and shows that combining both types of runs tests may provide some insight on the reason why rejection occurs, hence that Marden-type runs tests, despite their lack of consistency, also are of interest for practical purposes.

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Publisher Info
Paper provided by Université Libre de Bruxelles, Ecares in its series ECARES Working Papers with number 2009_002.

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Length: 44 pages
Date of creation: 2009
Date of revision:
Handle: RePEc:eca:wpaper:2009_002

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Related research
Keywords: elliptical distributions; interdirections; local asymptotic nrmality; multivariate signs; Shape matrix;

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References listed on IDEAS
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  1. Möttönen, J. & Hüsler, J. & Oja, H., 2003. "Multivariate nonparametric tests in a randomized complete block design," Journal of Multivariate Analysis, Elsevier, vol. 85(1), pages 106-129, April. [Downloadable!] (restricted)
  2. Thomas P. Hettmansperger, 2002. "A practical affine equivariant multivariate median," Biometrika, Oxford University Press for Biometrika Trust, vol. 89(4), pages 851-860, December.
  3. Lutz Dümbgen, 1998. "On Tyler's M-Functional of Scatter in High Dimension," Annals of the Institute of Statistical Mathematics, Springer, vol. 50(3), pages 471-491, September. [Downloadable!] (restricted)
  4. Haataja, Riina & Larocque, Denis & Nevalainen, Jaakko & Oja, Hannu, 2009. "A weighted multivariate signed-rank test for cluster-correlated data," Journal of Multivariate Analysis, Elsevier, vol. 100(6), pages 1107-1119, July. [Downloadable!] (restricted)
  5. Hallin, Marc & Paindaveine, Davy, 2005. "Affine-invariant aligned rank tests for the multivariate general linear model with VARMA errors," Journal of Multivariate Analysis, Elsevier, vol. 93(1), pages 122-163, March. [Downloadable!] (restricted)
  6. Taskinen, Sara & Kankainen, Annaliisa & Oja, Hannu, 2003. "Sign test of independence between two random vectors," Statistics & Probability Letters, Elsevier, vol. 62(1), pages 9-21, March. [Downloadable!] (restricted)
  7. Denis Larocque & Jaakko Nevalainen & Hannu Oja, 2007. "A weighted multivariate sign test for cluster-correlated data," Biometrika, Oxford University Press for Biometrika Trust, vol. 94(2), pages 267-283. [Downloadable!] (restricted)
  8. Lutz Dümbgen & David E. Tyler, 2005. "On the Breakdown Properties of Some Multivariate M-Functionals," Scandinavian Journal of Statistics, Danish Society for Theoretical Statistics, Finnish Statistical Society, Norwegian Statistical Association and Swedish Statistical Association, vol. 32(2), pages 247-264. [Downloadable!] (restricted)
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