Estimation of variances and covariances for high-dimensional data: A selective review

Tiejun Tong, Cheng Wang, Yuedong Wang*

*Corresponding author for this work

Research output: Contribution to journalJournal articlepeer-review

16 Citations (Scopus)
12 Downloads (Pure)


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.

Original languageEnglish
Pages (from-to)255-264
Number of pages10
JournalWiley Interdisciplinary Reviews: Computational Statistics
Issue number4
Early online date17 Jun 2014
Publication statusPublished - Jul 2014

Scopus Subject Areas

  • Statistics and Probability

User-Defined Keywords

  • Covariance matrix
  • High-dimensional data
  • Microarray data
  • Precision matrix
  • Shrinkage estimation
  • Sparse covariance matrix


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