On Variable Ordination of Modified Cholesky Decomposition for Sparse Covariance Matrix Estimation [article]

Xiaoning Kang, Xinwei Deng
2020 arXiv   pre-print
Estimation of large sparse covariance matrices is of great importance for statistical analysis, especially in the high-dimensional settings. The traditional approach such as the sample covariance matrix performs poorly due to the high dimensionality. The modified Cholesky decomposition (MCD) is a commonly used method for sparse covariance matrix estimation. However, the MCD method relies on the order of variables, which is often not available or cannot be pre-determined in practice. In this
more » ... , we solve this order issue by obtaining a set of covariance matrix estimates under different orders of variables used in the MCD. Then we consider an ensemble estimator as the "center" of such a set of covariance matrix estimates with respect to the Frobenius norm. The proposed method not only ensures the estimator to be positive definite, but also can capture the underlying sparse structure of the covariance matrix. Under some weak regularity conditions, we establish both algorithmic convergence and asymptotical convergence of the proposed method. The merits of the proposed method are illustrated through simulation studies and one real data example.
arXiv:1801.00380v3 fatcat:hbtulnx6fja35dgmrm3czqdf7u