Estimation of covariance and precision matrix, network structure, and a view toward systems biology
Kuismin, Markku O.; Sillanpää, Mikko J. (2017-09-28)
Kuismin, M. O. and Sillanpää, M. J. (2017), Estimation of covariance and precision matrix, network structure, and a view toward systems biology. WIREs Comput Stat, 9: e1415. https://doi.org/10.1002/wics.1415
© 2017 Wiley Periodicals, Inc. This is the peer reviewed version of the following article: Kuismin, M. O. and Sillanpää, M. J. (2017), Estimation of covariance and precision matrix, network structure, and a view toward systems biology. WIREs Comput Stat, 9: e1415. doi:10.1002/wics.1415, which has been published in final form at https://doi.org/10.1002/wics.1415. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Self-Archiving.
https://rightsstatements.org/vocab/InC/1.0/
https://urn.fi/URN:NBN:fi-fe201903229787
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Abstract
Covariance matrix and its inverse, known as the precision matrix, have many applications in multivariate analysis because their elements can exhibit the variance, correlation, covariance, and conditional independence between variables. The practice of estimating the precision matrix directly without involving any matrix inversion has obtained significant attention in the literature. We review the methods that have been implemented in R and their R packages, particularly when there are more variables than data samples and discuss ideas behind them. We describe how sparse precision matrix estimation methods can be used to infer network structure. Finally, we discuss methods that are suitable for gene coexpression network construction.
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