Meteorological networks optimization from a statistical point of view
Computational Statistics & Data Analysis - Optimal design and analysis of experiments
On the validity of Edgeworth and saddlepoint approximations
Journal of Multivariate Analysis
Refined approximations to permutation tests for multivariate inference
Computational Statistics & Data Analysis
The em algorithm for kernel matrix completion with auxiliary data
The Journal of Machine Learning Research
Some results on vector correlation
Computational Statistics & Data Analysis
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The relationship between two sets of variables defined for the same individuals can be evaluated by the RV coefficient. However, it is impossible to assess by the RV value alone whether or not the two sets of variables are significantly correlated, which is why a test is required. Asymptotic tests do exist but fail in many situations, hence the interest in permutation tests. However, the main drawbacks of the permutation tests are that they are time consuming. It is therefore interesting to approximate the permutation distribution with continuous distributions (without doing any permutation). The current approximations (normal approximation, a log-transformation and Pearson type III approximation) are discussed and a new one is described: an Edgeworth expansion. Finally, these different approximations are compared for both simulations and for a sensory example.