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In the multivariate case, the problem of optimal or near optimal interpolation is much more difficult.
In the multivariate case, a number of ways of combination is highlighted and the corresponding upper probabilities are computed.
In the multivariate case, the delay margin can be bounded by constraint on the upper principal gains on sensitivity and complementary sensitivity of the closed-loop.
We develop some general results in the multivariate case and carry out extensive simulations in a univariate model with partly known regressors and several error distributions.
In the multivariate case that is the basis for the present paperh, the estimation can be performed either using conditional mean models or fully parametric models.
In the multivariate case, In order to obtain equivariant estimates, it is of advantage to estimate location and dispersion simultaneously [28].
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We provide the interested reader with further properties of these stochastic orders in both the univariate and the multivariate cases.
There are typically several challenges in the studying and modeling of speech signals in the multivariate distribution case, e.g., the non-linear or linear inter-dimensional dependency, and the sparsity and complexity of the multidimensional space.
In the multivariate Gaussian case, these model parameters are defined as the parameters of the Gaussian mixture model, where the components determine the respective cluster arrays,.
In the multivariate Gaussian case, this parameter is defined as the responsibilities or posterior probability where z k denotes these latent variables and is defined as a binary vector.
In the multivariate analysis, cases with missing variables were excluded when that variable was considered as being part of the model.
More suggestions(14)
in the classical case
in the multivariate regression
in the multivariate context
in the finished case
in the present case
in the multivariate model
in the proper case
in the multivariate analysis
in the multivariate meta-analysis
in the multivariate setting
in the former case
in the first case
in the multivariate linear
in the ergative case
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