A0707
Title: Linear hypothesis testing on mean vectors for factor model in high-dimensional settings
Authors: Takahiro Nishiyama - Senshu University (Japan) [presenting]
Masashi Hyodo - Kanagawa University (Japan)
Abstract: For high-dimensional data, a general linear hypothesis testing problem on mean vectors of several populations is discussed, which includes many existing hypotheses about mean vectors as special cases. For this problem, based on $L^2$-type statistic, a testing procedure that accommodates a low-dimensional latent factor model under heteroscedasticity is proposed for this problem. Under a high-dimensional asymptotic regime, combined with weak technical conditions, it is shown that null limiting distributions of the test statistics follow a weighted mixture of chi-square distributions. Also, an evaluation of the finite sample performance of the proposed tests by a simulation study is provided.
