Sample covariance shrinkage for high dimensional dependent data

JOURNAL OF MULTIVARIATE ANALYSIS

卷:99

Article

Sancetta, Alessio

关键词: sample covariance matrix;    shrinkage;    weak dependence;   

DOI  :  10.1016/j.jmva.2007.06.004

来源: Elsevier

【 摘 要 】

For high dimensional data sets the sample covariance matrix is usually unbiased but noisy if the sample is not large enough. Shrinking the sample covariance towards a constrained, low dimensional estimator can be used to mitigate the sample variability. By doing so, we introduce bias, but reduce variance. In this paper, we give details on feasible optimal shrinkage allowing for time series dependent observations. (C) 2007 Elsevier Inc. All rights reserved.

【 授权许可】

Free