Article ID | Journal | Published Year | Pages | File Type |
---|---|---|---|---|
1145723 | Journal of Multivariate Analysis | 2014 | 6 Pages |
Abstract
In this article, we propose a computationally efficient approach to estimate (large) pp-dimensional covariance matrices of ordered (or longitudinal) data based on an independent sample of size nn. To do this, we construct the estimator based on a kk-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)(k+1)-dimensional covariance matrices. The resulting estimator is positive definite as long as k
Keywords
Related Topics
Physical Sciences and Engineering
Mathematics
Numerical Analysis
Authors
Y. Wang, M.J. Daniels,