Abstract
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 language | English |
|---|---|
| Pages (from-to) | 255-264 |
| Number of pages | 10 |
| Journal | Wiley Interdisciplinary Reviews: Computational Statistics |
| Volume | 6 |
| Issue number | 4 |
| Early online date | 17 Jun 2014 |
| DOIs | |
| Publication status | Published - 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