Department of Mathematics
Estimation of variances and covariances is required for many statistical methods such as t-test, principal component analysis and linear discriminant analysis. High-dimensional data such as gene expression microarray data and financial data pose challenges to traditional statistical and computational methods. In this paper, we review some recent developments in the estimation of variances, covariance matrix, and precision matrix, with emphasis on the applications to microarray data analysis.
Covariance matrix, High-dimensional data, Microarray data, Precision matrix, Shrinkage estimation, Sparse covariance matrix
Source Publication Title
Wiley Interdisciplinary Reviews: Computational Statistics
This is the peer reviewed version of the following article: Tong, T., Wang, C. and Wang, Y. (2014), Estimation of variances and covariances for high-dimensional data: a selective review. WIREs Comp Stat, 6: 255–264. doi: 10.1002/wics.1308, which has been published in final form at http://dx.doi.org/10.1002/wics.1308. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
Tiejun Tong's research was supported in part by Hong Kong Research Grant HKBU202711 and Hong Kong Baptist University FRG Grants FRG2/11-12/110 and FRGl/13-14/018. Cheng Wang's research was supported by NSF of China Grants 11101397, 71001095, and 11271347. Yuedong Wang's research was supported by NSF Grant DMS-07-06886. The authors thank the editor, the associate editor, and two referees for their constructive comments that led to a substantial improvement of this review article.
Link to Publisher's Edition
Tong, T., Wang, C., & Wang, Y. (2014). Estimation of variances and covariances for high-dimensional data: A selective review. Wiley Interdisciplinary Reviews: Computational Statistics, 6 (4), 255-264. https://doi.org/10.1002/wics.1308