Statistics and Its Interface
Volume 13 (2020)
High-dimensional Two-sample Precision Matrices Test: An Adaptive Approach through Multiplier Bootstrap
Pages: 37 – 48
Precision matrix, which is the inverse of covariance matrix, plays an important role in statistics, as it captures the partial correlation between variables. Testing the equality of two precision matrices in high-dimensional setting is a very challenging but meaningful problem, especially in the differential network modelling. To our best knowledge, existing test is only powerful for sparse alternative patterns where two precision matrices differ in a small number of elements. In this paper we propose a data-adaptive test which is powerful against either dense or sparse alternatives. Multiplier bootstrap approach is utilized to approximate the limiting distribution of the test statistic. Theoretical properties including asymptotic size and power of the test are investigated. Simulation study verifies that the data-adaptive test performs well under various alternative scenarios. The practical usefulness of the test is illustrated by applying it to a gene expression data set associated with lung cancer.
Differential network, High-dimensional, Precision matrix, Multiplier bootstrap.
2010 Mathematics Subject Classification
Primary 62H15. Secondary 62P10.
Yong He’s work is supported by National Science Foundation of China (11801316), Natural Science Foundation of Shandong Province (ZR2019QA002) and National Statistical Scientific Research Project (2018LY63).
Xinsheng Zhang’s work is supported by National Science Foundation of China (11571080, 11971116).
Received 8 June 2018
Accepted 25 July 2019
Published 7 November 2019