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Where λ is a shrinkage parameter.

Now, consider that the shrinkage parameter s = 0.05.

We shrink augmented simplex-centroid design, given in Table 11, using the shrinkage parameter s = 0.1.

In these biased classes, estimation of the shrinkage parameter plays an important role in data analyzing.

In Table 5, we have G-efficiency of the designs for different values of shrinkage parameter s (Fig. 3).

Now, shrink design in Table 2 is towards the centroid, with the shrinkage parameter s = 0.05 and re-parameterizes the co-ordinates using Eq. (2).

Another advantage of Optshrink is that there is no need to specify shrinkage parameter as is required in SVT [refer to (lambda _{2}) in (6)].

In our detection schemes, we will use αapprox in (26), the approximate value of the shrinkage parameter in the covariance matrix estimation process indicated in (25).

The necessary and sufficient condition for the superiority of the partially generalized QR-based estimator over partially generalized least-squares estimator for selecting the shrinkage parameter is obtained.

Also, some results demonstrating superiority of the QR-based estimator over ordinary least-squares estimator for selecting the shrinkage parameter are obtained.

In the first method, the threshold parameter and the shrinkage parameter are jointly optimized for each channel realization to minimize a Stein unbiased risk estimate (SURE) of the MSE of the MIMO channel estimation.

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