Computationally efficient banding of large covariance matrices for ordered data and connections to banding the inverse Cholesky factor.

ABSTRACT: In this article, we propose a computationally efficient approach to estimate (large) p-dimensional covariance matrices of ordered (or longitudinal) data based on an independent sample of size n. To do this, we construct the estimator based on a k-band partial autocorrelation matrix with the number of bands chosen using an exact multiple hypothesis testing procedure. This approach is considerably faster than many existing methods and only requires inversion of (k + 1)-dimensional covariance matrices. The resulting estimator is positive definite as long as k < n (where p can be larger than n). We make connections between this approach and banding the Cholesky factor of the modified Cholesky decomposition of the inverse covariance matrix (Wu an

SUBMITTER: Wang Y 

PROVIDER: S-EPMC4136395 | biostudies-literature | 2014 Sep

REPOSITORIES: biostudies-literature