Eigenvalue-regularized covariance matrix estimators for high-dimensional data
Covariance regularization is important when the dimension p of a covariance matrix is close to or even larger than the sample size n. This thesis concerns estimating large covariance matrix in both low and high frequency setting. First, we introduce an integration covariance matrix estimator which i...
| Main Author: | |
|---|---|
| Published: |
London School of Economics and Political Science (University of London)
2018
|
| Subjects: | |
| Online Access: | https://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.762917 |