典型文献
Projection-based High-dimensional Sign Test
文献摘要：
This article is concerned with the high-dimensional location testing problem.For high-dimensional settings,traditional multivariate-sign-based tests perform poorly or become infeasible since their Type Ⅰ error rates are far away from nominal levels.Several modifications have been proposed to address this challenging issue and shown to perform well.However,most of modified sign-based tests abandon all the correlation information,and this results in power loss in certain cases.We propose a projection weighted sign test to utilize the correlation information.Under mild conditions,we derive the optimal direction and weights with which the proposed projection test possesses asymptotically and locally best power under alternatives.Benefiting from using the sample-splitting idea for estimating the optimal direction,the proposed test is able to retain type-Ⅰ error rates pretty well with asymptotic distributions,while it can be also highly competitive in terms of robustness.Its advantage relative to existing methods is demonstrated in numerical simulations and a real data example.
文献关键词：
中图分类号：
作者姓名：
Hui CHEN;Chang Liang ZOU;Run Ze LI
作者机构：
School of Statistics and Data Science,Nankai University,Tianjin 300071,P.R.China;Department of Statistics and Methodology Center,Pennsylvania State University,201 Old Main,University Park,PA 16802,USA
文献出处：
引用格式：
[1]Hui CHEN;Chang Liang ZOU;Run Ze LI-.Projection-based High-dimensional Sign Test)[J].数学学报（英文版）,2022(04):683-708
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AB值：
0.694535
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