Description Usage Arguments Details Value References Examples

This algorithm seeks to find a covariance (dense) estimate
that (asymptotically) minimizes the mean-squared error (MSE) obtained by
linear shrinkage problem as proposed by Ledoit and Wolf (LW).
It is effectively a interpolation/mix of the sample ML
estimate of the covariance matrix, *S*, and the most well-conditioned
(and naive) estimate *F = 1/p tr(S) I*.

1 2 3 |

`X` |
The data matrix of size |

`method` |
The method of estimating the optimal interpolating parameter. The default is OAS. |

The improved estimate using Rao-Blackwell theorem, abbreviated RBLW, and the oracle approximating shrinkage (OAS) are also implemented. The algorithm seeks a solution to the problem:

*
minimize E[ || Sigma_O - Sigma ||^2 ] w.r.t. rho*

*
s.t. Sigma_O = (1-rho)*S + rho*F*

using various methods
The interpolated *rho* value used is always
*min(rho,1)*.
More information can be found in the given reference.

A `p`

by `p`

numeric matrix with two extra attributes giving
the used mixture (*rho*) and the method.

Ledoit, O., & Wolf, M. (2004). A well-conditioned estimator for large-dimensional covariance matrices. Journal of Multivariate Analysis, 88(2), 365-411. doi:10.1016/S0047-259X(03)00096-4

Chen, Y., & Wiesel, A. (2010). Shrinkage algorithms for MMSE covariance estimation. Signal Processing, IEEE, 58(734), 1-28. Methodology; Computation. http://arxiv.org/abs/0907.4698

Schafer, J., & Strimmer, K. (2005). A shrinkage approach to large-scale covariance matrix estimation and implications for functional genomics. Statistical Applications in Genetics and Molecular Biology, 4(1). http://www.stat.wisc.edu/courses/st992-newton/smmb/files/expression/shrinkcov2005.pdf

1 2 3 4 5 6 | ```
n <- 3
X <- createData(n, 5)
cov(X)
Scov(X, method = "OAS")
Scov(X, method = "RBLW")
Scov(X, method = "LW")
``` |

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