Department of Mathematics
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 Cramér–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
Source Publication Title
Computational Statistics & Data Analysis
Copyright © 2009 Elsevier B.V. All rights reserved.
This research was partially supported by grants from the Research Grants Council of the Hong Kong Special Administrative Region, China (Project Numbers HKBU200605 and HKBU200807) and an FRG grant of the Hong Kong Baptist University.
Link to Publisher's Edition
Chiu, S., & Liu, K. (2009). Generalized Cramér–von Mises goodness-of-fit tests for multivariate distributions. Computational Statistics & Data Analysis, 53 (11), 3817-3834. https://doi.org/10.1016/j.csda.2009.04.004