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The model parameters needed for the latter matrix are estimated by REML.
Finally, the rotation and translation matrix are estimated by the quaternion, and an iterative algorithm is used to improve the registration accuracy.
Based on the proposed strategy, physically interpretable mass and stiffness matrices, as well as non-proportional damping matrix are estimated solely from the measured experimental data.
The columns of the mixing matrix are estimated by finding picks in a 2D histogram of amplitude-delay estimates.
The elements of the channel matrix are estimated during the uplink pilot phase and are given by (8).
The parameters in the Q matrix are estimated by maximizing the likelihood function given in Equation (1).
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The inverse of the covariance matrix is estimated via the shrinkage estimator [ 96- 99].
We propose a modified QIF method, in which the optimal weighting matrix is estimated by a linear shrinkage estimator, replacing the sample covariance matrix.
The asymptotic variance matrix is estimated with the usual "sandwich" estimator [ 8].
Therefore, an asymptotic covariance matrix is estimated by means of the robust sandwich estimator proposed by Mullahy ([2010]).
Then, the dynamic covariance matrix is estimated by the cross-covariance matrices.
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