Sample covariance shrinkage for high dimensional dependent data

Alessio Sancetta

Journal of Multivariate Analysis, 2008, vol. 99, issue 5, 949-967

Abstract: 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.

Keywords: Sample; covariance; matrix; Shrinkage; Weak; dependence (search for similar items in EconPapers)
Date: 2008
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Citations: View citations in EconPapers (13)

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Related works:
Working Paper: Sample Covariance Shrinkage for High Dimensional Dependent Data (2006)
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