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This may be accomplished in a nonparametric and nonstationary manner using a surrogate analysis approach [32], for example.

Conflict studies are also presented as a surrogate analysis method for hazardous site identification, or even as a posttreatment evaluation tool when crash data are not yet available.

The present study deals with the numerical optimization of microchannel heat sink with the help of surrogate analysis and evolutionary algorithm.

This surrogate analysis destroys the true evaluative feedback from the NAcc.

Compared to a surrogate analysis, the real NAcc evaluative feedback provided a useful method for adapting the Actor to solve a 3-D reaching task.

In order to show the effectiveness of the Critic's response based on the real NAcc neural activity, we performed a surrogate analysis in which the evaluative feedback from Critic was replaced by a random sequence of feedback values (+1 or −1).

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We utilized four data decomposition methods – independent surrogate variable analysis (ISVA), surrogate variable analysis (SVA), principal component analysis (PCA) and PEER [ 13, 22] – to account for unknown covariates.

Section 2.1 describes a time-to-initiation and time-to-cessation of surrogate drug analysis and Section 2.2 an analysis based on the quantity of the surrogate drug use.

In such cases, two methods, surrogate variable analysis (SVA) (Leek and Storey, 2007) and independent surrogate variable analysis (ISVA) (Teschendorff et al, 2011), also available as the ISVA R package, are very useful.

Surrogate variable analysis (SVA)[13], combines singular value decomposition (SVD) and a linear model analysis to estimate the eigenvalues from a residual expression matrix from which biological variation has already been removed.

For the surrogate data analysis, forecast error and time reversal asymmetry statistics are used.

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