Random number generation and quasi-Monte Carlo methods
Random number generation and quasi-Monte Carlo methods
A generalized discrepancy and quadrature error bound
Mathematics of Computation
Testing multivariate uniformity and its applications
Mathematics of Computation
An affine invariant multiple test procedure for assessing multivariate normality
Computational Statistics & Data Analysis
Goodness-of-fit tests for multivariate Laplace distributions
Mathematical and Computer Modelling: An International Journal
Generating beta random numbers and Dirichlet random vectors in R: The package rBeta2009
Computational Statistics & Data Analysis
| Hi-index | 0.03 |
A class of statistics for testing the goodness-of-fit for any multivariate continuous distribution is proposed. These statistics consider not only the goodness-of-fit of the joint distribution but also the goodness-of-fit of all marginal distributions, and can be regarded as generalizations of the multivariate Cramer-von Mises statistic. Simulation shows that these generalizations, using the Monte Carlo test procedure to approximate their finite-sample p-values, are more powerful than the multivariate Kolmogorov-Smirnov statistic.