A consistent test of spherical symmetry for multivariate and high-dimensional data via data augmentation

Abstract: We develop a test for spherical symmetry of a multivariate distribution $P$ that works even when the dimension of the data $d$ is larger than the sample size $n$. We propose a non-negative measure $\zeta(P)$ such that $\zeta(P)=0$ if and only if $P$ is spherically symmetric. We construct a consistent estimator of $\zeta(P)$ using the data augmentation method and investigate its large sample properties. The proposed test based on this estimator is calibrated using a novel resampling algorithm. Our test controls the Type-I error, and it is consistent against general alternatives. We also study its behaviour for a sequence of alternatives $(1-\delta_n) F+\delta_n G$, where $\zeta(G)=0$ but $\zeta(F)>0$, and $\delta_n \in [0,1]$. When $\lim\sup\delta_n<1$, for any $G$, the power of our test converges to unity as $n$ increases. However, if $\lim\sup\delta_n=1$, the asymptotic power of our test depends on $\lim n(1-\delta_n)^2$. We establish this by proving the minimax rate optimality of our test over a suitable class of alternatives and showing that it is Pitman efficient when $\lim n(1-\delta_n)^2>0$. Moreover, our test is provably consistent for high-dimensional data even when $d$ is larger than $n$. Our numerical results amply demonstrate the superiority of the proposed test over some state-of-the-art methods.