Translator Disclaimer
2013 Rank-based score tests for high-dimensional regression coefficients
Long Feng, Changliang Zou, Zhaojun Wang, Bin Chen
Electron. J. Statist. 7: 2131-2149 (2013). DOI: 10.1214/13-EJS839


This article is concerned with simultaneous tests on linear regression coefficients in high-dimensional settings. When the dimensionality is larger than the sample size, the classic $F$-test is not applicable since the sample covariance matrix is not invertible. Recently, [5] and [17] proposed testing procedures by excluding the inverse term in $F$-statistics. However, the efficiency of such $F$-statistic-based methods is adversely affected by outlying observations and heavy tailed distributions. To overcome this issue, we propose a robust score test based on rank regression. The asymptotic distributions of the proposed test statistic under the high-dimensional null and alternative hypotheses are established. Its asymptotic relative efficiency with respect to [17]’s test is closely related to that of the Wilcoxon test in comparison with the $t$-test. Simulation studies are conducted to compare the proposed procedure with other existing testing procedures and show that our procedure is generally more robust in both sizes and powers.


Download Citation

Long Feng. Changliang Zou. Zhaojun Wang. Bin Chen. "Rank-based score tests for high-dimensional regression coefficients." Electron. J. Statist. 7 2131 - 2149, 2013.


Published: 2013
First available in Project Euclid: 23 August 2013

zbMATH: 1349.62218
MathSciNet: MR3104951
Digital Object Identifier: 10.1214/13-EJS839

Primary: 62H15
Secondary: 62G20, 62J05

Rights: Copyright © 2013 The Institute of Mathematical Statistics and the Bernoulli Society


Back to Top