TY - JOUR
AU - Wang, Zhaojun
AB - This article concerns tests for sphericity in cases where the data dimension is larger than the sample size. The existing multivariate sign-based procedure (Hallin & Paindaveine, 2006) for sphericity is not robust with respect to high dimensionality, producing tests with Type I error rates that are much larger than the nominal levels. This is mainly due to bias from estimating the location parameter. We develop a correction that makes the existing test statistic robust with respect to high dimensionality, and show that the proposed test statistic is asymptotically normal under elliptical distributions. The proposed method allows the dimensionality to increase as the square of the sample size. Simulations demonstrate that it has good size and power in a wide range of settings.
TI - Multivariate sign-based high-dimensional tests for sphericity
JF - Biometrika
DO - 10.1093/biomet/ast040
DA - 2014-03-13
UR - https://www.deepdyve.com/lp/oxford-university-press/multivariate-sign-based-high-dimensional-tests-for-sphericity-kLdb0hhNmC
SP - 229
EP - 236
VL - 101
IS - 1
DP - DeepDyve
ER -