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

In: Contemporary Experimental Design, Multivariate Analysis and Data Mining

Author

Listed:
  • Wanjun Liu

    (Pennsylvania State University, Department of Statistics)

  • Runze Li

    (Pennsylvania State University, Department of Statistics)

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.

Suggested Citation

  • Wanjun Liu & Runze Li, 2020. "Projection Test with Sparse Optimal Direction for High-Dimensional One Sample Mean Problem," Springer Books, in: Jianqing Fan & Jianxin Pan (ed.), Contemporary Experimental Design, Multivariate Analysis and Data Mining, chapter 0, pages 295-309, Springer.
    • Handle: RePEc:spr:sprchp:978-3-030-46161-4_19
    DOI: 10.1007/978-3-030-46161-4_19
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