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Then, maximizing (5) subject to unit variance constraint E{|w H y|2} = 1, the weight vector w of the classical complex-valued FastICA can obtain the optimization problem [36] maximize w E G w H y 2, subject t o E w H y 2 = 1.
From this bootstrap sample, the model (4) is estimated by maximizing (5) in which the simulated parameters β ^ b e ∗ are used instead of β ^ e. β ^ b r is the resulting estimation of the relative regression coefficients.
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The probabilities maximizing (3) are given as p → 1 n : = 1 n, 1 n, …, 1 n.
To control MNC size for maximizing T2 relaxivity, the polysorbate 80 concentration was adjusted.
Therefore, the optimal (X n )∗ that maximizing (14) can be obtained by (15).
Therefore, VM presents a balance between minimizing E(x 2) and maximizing E x 2 in this scenario.
We obtain the optimal solution w* by maximizing (3.13) using the same principle.
We estimate the parameters of the model by maximizing (2) with respect to these parameters.
Our goal is to achieve maximum reliability on a path, which is equivalent to maximizing (3).
By maximizing (18), the maximum likelihood estimates of ω k, a k, and, are obtained.
The maximum likelihood estimation of model parameters can be computed by maximizing (14) numerically.
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CEO of Professional Science Editing for Scientists @ prosciediting.com