The Annals of Statistics
- Ann. Statist.
- Volume 41, Number 4 (2013), 2075-2096.
Tests for covariance matrix with fixed or divergent dimension
Testing covariance structure is of importance in many areas of statistical analysis, such as microarray analysis and signal processing. Conventional tests for finite-dimensional covariance cannot be applied to high-dimensional data in general, and tests for high-dimensional covariance in the literature usually depend on some special structure of the matrix. In this paper, we propose some empirical likelihood ratio tests for testing whether a covariance matrix equals a given one or has a banded structure. The asymptotic distributions of the new tests are independent of the dimension.
Ann. Statist. Volume 41, Number 4 (2013), 2075-2096.
First available in Project Euclid: 23 October 2013
Permanent link to this document
Digital Object Identifier
Mathematical Reviews number (MathSciNet)
Zentralblatt MATH identifier
Primary: 62F03: Hypothesis testing
Secondary: 62F40: Bootstrap, jackknife and other resampling methods
Zhang, Rongmao; Peng, Liang; Wang, Ruodu. Tests for covariance matrix with fixed or divergent dimension. Ann. Statist. 41 (2013), no. 4, 2075--2096. doi:10.1214/13-AOS1136. https://projecteuclid.org/euclid.aos/1382547513
- Supplementary material: Supplement to “Tests for covariance matrix with fixed or divergent dimension”. This supplementary file contains detailed proofs of Lemmas 4.1–4.5 used in Section 4.