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Multivariate Statistical Simulation
James E. Gentle,Mark E. Johnson +1 more
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This article is published in Journal of Business & Economic Statistics.The article was published on 1988-01-01. It has received 173 citations till now. The article focuses on the topics: Multivariate statistics & Multivariate analysis.read more
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Developing joint probability distributions of soil water retention characteristics
TL;DR: In this paper, a method is presented for developing probability density functions for parameters of soil moisture relationships of capillary head [h(θ)] and hydraulic conductivity [K(α), which are required for the assessment of water flow and solute transport in unsaturated media.
Journal ArticleDOI
GSTAT: a program for geostatistical modelling, prediction and simulation
Edzer Pebesma,C. G. Wesseling +1 more
TL;DR: The class of problems gstat can solve is described, and aspects of efficiency and implementation, managing geostatistical projects, and relevant technical details are addressed.
Journal ArticleDOI
The Meta-elliptical Distributions with Given Marginals
TL;DR: In this paper, a meta-elliptical distribution is proposed, which has the same Kendall's rank correlation coefficient as meta-Gaussian distributions, and the corresponding analytic forms of the density, conditional distribution functions, and dependence properties are derived.
Journal ArticleDOI
On a new multivariate two-sample test
L. Baringhaus,C. Franz +1 more
TL;DR: In this paper, the authors proposed a new test for the multivariate two-sample problem, where the test statistic is the difference of the sum of all the Euclidean interpoint distances between the random variables from the two different samples and one-half of the two corresponding sums of distances of the variables within the same sample.
Journal ArticleDOI
On the Use of Copulas in Hydrology: Theory and Practice
TL;DR: In this article, the use of copulas in hydrological modeling has been explored and many important results still are to be discovered and/or derived, such as: (1) the calculation of conditional probabilities and their use in bivariate simulation; (2) the calculated level curves of joint distributions; (3) the return periods of bivariate events, both in the conditional and unconditional cases; (4) the definition and calculation of the secondary return period; (5) a trivariate model for the temporal structure of the sequence of storms; (6) the
References
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Journal ArticleDOI
Developing joint probability distributions of soil water retention characteristics
TL;DR: In this paper, a method is presented for developing probability density functions for parameters of soil moisture relationships of capillary head [h(θ)] and hydraulic conductivity [K(α), which are required for the assessment of water flow and solute transport in unsaturated media.
Journal ArticleDOI
GSTAT: a program for geostatistical modelling, prediction and simulation
Edzer Pebesma,C. G. Wesseling +1 more
TL;DR: The class of problems gstat can solve is described, and aspects of efficiency and implementation, managing geostatistical projects, and relevant technical details are addressed.
Journal ArticleDOI
The Meta-elliptical Distributions with Given Marginals
TL;DR: In this paper, a meta-elliptical distribution is proposed, which has the same Kendall's rank correlation coefficient as meta-Gaussian distributions, and the corresponding analytic forms of the density, conditional distribution functions, and dependence properties are derived.
Journal ArticleDOI
On a new multivariate two-sample test
L. Baringhaus,C. Franz +1 more
TL;DR: In this paper, the authors proposed a new test for the multivariate two-sample problem, where the test statistic is the difference of the sum of all the Euclidean interpoint distances between the random variables from the two different samples and one-half of the two corresponding sums of distances of the variables within the same sample.
Methods for the Computation of Multivariate t-Probabilities ∗
TL;DR: In this article, the authors compared methods for the numerical computation of multivariate t-probabilities for hyperrectangular integration regions based on acceptance-rejection, spherical-radial transformations and separation-of-variables transformations, and showed that the most efficient numerical methods use a transformation developed by Genz (1992) for multivariate normal probabilities.