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Models were fitted with and without multiple variances, and comparisons were made with the Akaike Information Criterion (Akaike 1974) to select the optimal model.
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However, like higher-order moments, multiple variances are not additive, and thus, the effective spatial resolution enhancement is limited.
Statistical analyses were conducted using one-way ANOVA with Dunnett׳s posttest, and multiple variances were analyzed by two-way ANOVA (GraphPad Prism).
Data were compared by one-way ANOVA and significant differences obtained using the Tukey multiple variances post hoc test.
However, in multiple variance analysis, FM and WP were similar, and significantly different than RP and NP.
Instead, multiple variances are determined for different frequency ranges resulting in a multidimensional feature vector.
Individuals with WP and FM had no significant differences in the assessed domains in multiple variances analysis.
Groups were compared using the Kruskal-Wallis Anova and multiple variance analysis.
We checked the performance of the results over multiple variance-covariance matrices ψ and report the results for two representative cases where the variance-covariance parameter matrices are multiples of ψ, which were estimated directly from the data: and The estimated biases and standard deviations of the fixed effects parameters are reported in Tables 6 and 7 respectively.
It is based on a multiple variance calculation.
Both formulations result in the definition of multiple variance components.
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