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.
Most of the works on control charts in multivariate case are on problem of monitoring mean vector of the process.
In multivariate case, with (Xin mathbf {R}^{n}), (ngeq1), there is an old and seemingly forgotten mean value theorem by Kowalewski [1, 2] as follows.
In univariate analyses, the features are limited to the variable being forecasted, while in multivariate case, any variable that is thought to be related to the output is considered a feature (an input of the model).
(1999), it is also possible to generalize the local regression procedure employed in the NN non-parametric regression to the multivariate case using the information contained in several time series.
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.
In the multivariate case, we provide a quasi-binomial approximation to the distribution of both the resubstitution and leave-one-out error estimators for LDA, under a common but otherwise arbitrary class covariance matrix, which is assumed to be known in the design of the LDA discriminant.
In the multivariate case, the problem of optimal or near optimal interpolation is much more difficult.
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.
