Projection Test for Mean Vector in High Dimensions
Wanjun Liu, Xiufan Yu, Wei Zhong, Runze Li
Abstract
-test under normality assumption. To mitigate the power loss due to data-splitting, we further propose an online framework, which iteratively updates the estimation of projection direction when new observations arrive. We show that this online-style projection test asymptotically converges to the standard normal distribution. Various simulation studies as well as a real data example show that the proposed online-style projection test retains the type I error rate well and is more powerful than other existing tests.
Topics & Concepts
Projection (relational algebra)MathematicsVector projectionDimension (graph theory)Random projectionProjection methodAlgorithmMathematical optimizationDykstra's projection algorithmGeometryCombinatoricsStatistical Methods and InferenceStatistical Distribution Estimation and ApplicationsStatistical Methods and Bayesian Inference