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For the first case, as the right-hand side matrix is diagonalized, the solution of (2)is as (1).
After pivotal expansion, another function Q 3 = b is introduced to the right side matrix of Eq. (21).
Carry pivotal expansion on the terms in the fourth row of the right side matrix of Eq. (21), we get Eq. (22) (Fig. 7).
Here A is the Jacobian matrix (coefficients of the endogenous variables of the model), X represents the matrix of endogenous variables (prices and quantities) while the right-hand side matrix denotes the exogenous variables (policy shocks).
In Eq. (14), the left-hand side matrix shows the dissimilarities among data points and the right vector in the Eq. (14) describes the dissimilarities between the estimation point and the data points.
Under the assumption of full channel state information (CSI) at the transmit side as well as at the receive side, matrix theory, including several decomposition approaches, is often used as a basic tool to facilitate the precoder design and performance analysis for different types of communication scenarios.
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The first right-hand-side matrix product takes into account the case where 1 packet is transmitted.
The segregate analysis of the modal equations prevents assembling and inversion of a large left-hand-side matrix, when high number of modes are involved.
The right-side matrix of (2.4) is called the reduced polynomial of A. If the order of A t t is not considered, (2.4) has nothing to do with the choice of P. So (2.4) is a unique definite partition N 1, N 2, …, N K on a set N = { 1, 2, …, n } corresponding to the subscripted set of A 11, A 22, …, A K K.
At time point 2, it was not possible to fit random intercepts because the G-side matrix was always not positive definite.
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