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The dispersion part is solved by the central difference method and the propagation part is solved implicitly by using the Gauss Seidel iteration method.
Models were compared in such a way that the reasons of the discrepancies between them can be assessed (i.e., if they are due to the hydrodynamic part, the dispersion part, and the ultimate reasons).
In this case, generated changes lead to decrease the dispersion part of surface energy.
We consider a model consisting of a mean part and a dispersion part.
Now we extend the model further and include random effects in the dispersion part.
Maximum likelihood estimates for the fixed effects in the dispersion part can be achieved by using a gamma GLM with squared residuals as response.
Similar(48)
The operator splitting approach is adopted in constructing the method in which the conservation and dispersion parts of the equation are alternatively solved; our numerical device is applied in solving the conservation part of the equation.
The fixed effects in the mean and dispersion parts are b and b d, respectively.
We start by considering a linear model with only fixed effects both in the mean and dispersion parts.
A hierarchical model is assumed so that y| u ~ f m and u ~ f d where f m and f d are specified distributions for the mean and dispersion parts of the model.
In the present paper, u and u d are treated as non-correlated so that (5) We allow for more than one random effect in the mean and dispersion parts of the model.
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