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Estimation of covariance and precision matrix, network structure, and a view toward systems biology
Author(s) -
Kuismin Markku O.,
Sillanpää Mikko J.
Publication year - 2017
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.1415
Subject(s) - covariance matrix , estimation of covariance matrices , covariance , computer science , conditional independence , multivariate statistics , algorithm , matrix (chemical analysis) , graphical model , conditional dependence , independence (probability theory) , data mining , statistics , mathematics , artificial intelligence , machine learning , chemistry , chromatography
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. WIREs Comput Stat 2017, 9:e1415. doi: 10.1002/wics.1415 This article is categorized under: Statistical Models > Linear Models Applications of Computational Statistics > Computational and Molecular Biology Statistical and Graphical Methods of Data Analysis > Multivariate Analysis