Statistics and Its Interface

Volume 13 (2020)

Number 1

High-dimensional Two-sample Precision Matrices Test: An Adaptive Approach through Multiplier Bootstrap

Pages: 37 – 48

DOI: https://dx.doi.org/10.4310/SII.2020.v13.n1.a4

Authors

Mingjuan Zhang (School of Statistics and Mathematics, Shanghai Lixin University of Accounting and Finance, Shanghai, China)

Yong He (School of Statistics, Shandong University of Finance and Economics, Jinan, China)

Cheng Zhou (School of Management, Fudan University, Shanghai, China)

Xinsheng Zhang (School of Management, Fudan University, Shanghai, China)

Abstract

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.

Keywords

Differential network, High-dimensional, Precision matrix, Multiplier bootstrap.

2010 Mathematics Subject Classification

Primary 62H15. Secondary 62P10.

Received 8 June 2018

Accepted 25 July 2019

Published 7 November 2019