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The approach of modelling the correlation as a hyperbolic function of a stochastic process has been recently proposed.
Several alternatives have been considered in modelling the correlation time of the ionospheric residuals, including the white noise model (WN), Gauss-Markov process, and the random walk model.
The model takes advantage of sample information from preceding time periods through an appropriate time series model and from other domains by modelling the correlation between the trend components of the time series models for the different domains.
Through mixed effects modelling, the correlation structure of the data can be explicitly examined by researchers.
The use of multilevel models corrects this by modelling the correlation between the control sets.
A marginal model takes into account the repeated measurement structure of the data by modelling the correlation structure; it is an appropriate choice of model when focusing on population averages [ 32].
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In addition, [26] considers adopting a GARCH-type structure while modelling the correlations' dynamics.
Choquet integral [15] is a useful tool to model the correlation or inter-dependence.
Copula functions are mainly used in modeling the correlation between stochastic variables.
A virtual channel is used to model the correlation between the DCT coefficients of the original and SI frames.
The third step has the target to model the correlation noise n (or residue) at DCT band level between the decoder-generated SI and the original WZ frame.
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