Recent advances in shrinkage-based high-dimensional inference

Olha Bodnar, Taras Bodnar and Nestor Parolya

Journal of Multivariate Analysis, 2022, vol. 188, issue C

Abstract: Recently, the shrinkage approach has increased its popularity in theoretical and applied statistics, especially, when point estimators for high-dimensional quantities have to be constructed. A shrinkage estimator is usually obtained by shrinking the sample estimator towards a deterministic target. This allows to reduce the high volatility that is commonly present in the sample estimator by introducing a bias such that the mean-square error of the shrinkage estimator becomes smaller than the one of the corresponding sample estimator. The procedure has shown great advantages especially in the high-dimensional problems where, in general case, the sample estimators are not consistent without imposing structural assumptions on model parameters.

Keywords: Covariance matrix; High-dimensional asymptotics; High-dimensional optimal portfolio; Mean vector; Precision matrix; Random matrix theory; Shrinkage estimation (search for similar items in EconPapers)
Date: 2022
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Citations: View citations in EconPapers (4)

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DOI: 10.1016/j.jmva.2021.104826

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