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In a multivariate case, Sklar's theorem is as follows: Let F be an n-dimensional cumulative distribution function (CDF) with margins F 1,…, F n. Then there exists a function C :[0,1] n →[0,1] such that: F left({x_{1},ldots,x_{n}} right) = Cleft({F_{1} left(x_{1} right),ldots,F_{n} left(x_{n} right)} right).
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In the multivariate case, a number of ways of combination is highlighted and the corresponding upper probabilities are computed.
In the multivariate case, an additional source of genetic constraints may arise from genetic correlations.
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.
Surprisingly, there was a tendency to shift from a slight increase in ORIF for males with the bivariate case to a slight preference for females in the multivariate case.
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.
Thus, it seems an appropriate form to consider for the prior for θ in the multivariate case.
In the multivariate case, the problem of optimal or near optimal interpolation is much more difficult.
In the multivariate case, In order to obtain equivariant estimates, it is of advantage to estimate location and dispersion simultaneously [28].
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 that is the basis for the present paperh, the estimation can be performed either using conditional mean models or fully parametric models.
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