Projection Test with Sparse Optimal Direction for High-Dimensional One Sample Mean Problem

Wanjun Liu () and Runze Li ()
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Wanjun Liu: Pennsylvania State University, Department of Statistics
Runze Li: Pennsylvania State University, Department of Statistics

Chapter Chapter 19 in Contemporary Experimental Design, Multivariate Analysis and Data Mining, 2020, pp 295-309 from Springer

Abstract: Abstract Testing whether the mean vector from some population is zero or not is a fundamental problem in statistics. In the high-dimensional regime, where the dimension of data p is greater than the sample size n, traditional methods such as Hotelling’s $$T^2$$ test cannot be directly applied. One can project the high-dimensional vector onto a space of low dimension and then traditional methods can be applied. In this paper, we propose a projection test based on a new estimation of the optimal projection direction $$\varSigma ^{-1}\mu $$. Under the assumption that the optimal projection $$\varSigma ^{-1}\mu $$ is sparse, we use a regularized quadratic programming with nonconvex penalty and linear constraint to estimate it. Simulation studies and real data analysis are conducted to examine the finite sample performance of different tests in terms of type I error and power.

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:spr:sprchp:978-3-030-46161-4_19

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DOI: 10.1007/978-3-030-46161-4_19

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