Premium
Estimation of variances and covariances for high‐dimensional data: a selective review
Author(s) -
Tong Tiejun,
Wang Cheng,
Wang Yuedong
Publication year - 2014
Publication title -
wiley interdisciplinary reviews: computational statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.693
H-Index - 38
eISSN - 1939-0068
pISSN - 1939-5108
DOI - 10.1002/wics.1308
Subject(s) - principal component analysis , covariance matrix , linear discriminant analysis , computer science , multivariate statistics , data matrix , statistical hypothesis testing , covariance , data mining , statistics , mathematics , algorithm , biology , clade , biochemistry , gene , phylogenetic tree
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. WIREs Comput Stat 2014, 6:255–264. doi: 10.1002/wics.1308 This article is categorized under: Data: Types and Structure > Microarrays Statistical and Graphical Methods of Data Analysis > Multivariate Analysis