Goodness-of-fit tests for multivariate stable distributions based on the empirical characteristic function

We consider goodness-of-fit testing for multivariate stable distributions. The proposed test statistics exploit a characterizing property of the characteristic function of these distributions and are consistent under some conditions. The asymptotic distribution is derived under the null hypothesis a...

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Bibliographic Details
Published in:Journal of multivariate analysis 2015-09, Vol.140, p.171-192
Main Authors: Meintanis, Simos G., Ngatchou-Wandji, Joseph, Taufer, Emanuele
Format: Article
Language:English
Subjects:
Asymptotic methods
Characteristic function
Characterization
Goodness-of-fit
Multivariate analysis
Multivariate stable distribution
Probability distribution
Simulation
Statistics
Statistics Theory
Studies
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Summary:We consider goodness-of-fit testing for multivariate stable distributions. The proposed test statistics exploit a characterizing property of the characteristic function of these distributions and are consistent under some conditions. The asymptotic distribution is derived under the null hypothesis as well as under local alternatives. Conditions for an asymptotic null distribution free of parameters and for affine invariance are provided. Computational issues are discussed in detail and simulations show that with proper choice of the user parameters involved, the new tests lead to powerful omnibus procedures for the problem at hand.
ISSN:0047-259X
1095-7243
DOI:10.1016/j.jmva.2015.05.006