TY - JOUR
T1 - Generalized Cramér–von Mises goodness-of-fit tests for multivariate distributions
AU - Chiu, Sung Nok
AU - Liu, Kwong Ip
N1 - Funding Information:
We thank the referees for their helpful suggestions and drawing our attention to the work by Liang et al. (2000). 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.
Publisher copyright:
© 2009 Elsevier B.V. All rights reserved.
PY - 2009/9/1
Y1 - 2009/9/1
N2 - 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 -values, are more powerful than the multivariate Kolmogorov–Smirnov statistic.
AB - 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 -values, are more powerful than the multivariate Kolmogorov–Smirnov statistic.
UR - https://doi.org/10.1016/j.csda.2009.09.013
UR - http://www.scopus.com/inward/record.url?scp=67349267793&partnerID=8YFLogxK
U2 - 10.1016/j.csda.2009.04.004
DO - 10.1016/j.csda.2009.04.004
M3 - Journal article
AN - SCOPUS:67349267793
SN - 0167-9473
VL - 53
SP - 3817
EP - 3834
JO - Computational Statistics and Data Analysis
JF - Computational Statistics and Data Analysis
IS - 11
ER -