Premium
High‐dimensional covariance estimation for Gaussian directed acyclic graph models with given order
Author(s) -
Taylor Jerome,
Khare Kshitij
Publication year - 2019
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.1468
Subject(s) - cholesky decomposition , covariance , estimation of covariance matrices , covariance matrix , multivariate statistics , covariance function , graphical model , computer science , mathematics , data set , algorithm , gaussian , statistics , physics , quantum mechanics , eigenvalues and eigenvectors
The covariance matrix is a fundamental quantity that helps us understand the nature of relationships among variables in a multivariate data set. Estimating the covariance matrix can be challenging in modern applications where the number of variables is often larger than the number of samples. In this paper, we review methods which tackle this challenge by inducing sparsity in the Cholesky parameter of the inverse covariance matrix. This article is categorized under: Algorithms and Computational Methods > Numerical Methods Statistical and Graphical Methods of Data Analysis > Multivariate Analysis Statistical and Graphical Methods of Data Analysis > Analysis of High Dimensional Data