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This new terminology clearly distinguishes it from the ubiquitous class of "elliptical copulas" that is generated by the multivariate elliptical distributions in any dimension.
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First, the multivariate distribution of the asset returns is fitted by a multivariate distribution.
Rather correlation is the canonical measure of the stochastic dependence used with normal (elliptical) distributions and is strongly affected by extreme endpoints.
One can also extend this results to a class of skew-elliptical distributions starting from the discrete scale mixture.
Multivariate distributions: joint, conditional, and marginal distributions, independence, transformations, Multinomial, Multivariate Normal.
In structural system reliability theory, the evaluation of multivariate normal distributions is an important problem.
Multivariate income distributions are also of interest and, in that arena, a hierarchy of multivariate Pareto distributions is available, paralleling and closely related to the univariate hierarchy.
Multivariate extreme-value distributions are established, and the properties of these distributions are investigated.
However, due to the complexity of multivariate distributions, existing methods in univariate processes cannot be readily extended to multivariate processes.
In other words, the multivariate distribution is coupled to its marginal distributions.
Discrete and continuous, univariate and multivariate distributions.
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CEO of Professional Science Editing for Scientists @ prosciediting.com