With a large number of variables measuring different aspects of a same theme, we would like to summarize the information in a limited number of components, i.e. linear combinations of the original variables. Among linear dimension reduction techniques, principal component analysis is optimal in...

We introduce a variant of Goodman and Kruskal's gamma coefficient which can be interpreted as a measure of "proportional-reduction-in-variation". Our goal was to provide the gamma coefficient with an R2-like interpretation, which makes interpretation of intermediate values easier.

In this paper, we propose simple exact procedures for testing both a location shift and/or a scale change between two multivariate distributions. Our tests are strictly distribution-free and can be made either scale invariant or rotation invariant. Our approach combines a generalization of the...

In this paper the interest is in testing whether a regression function is a polynomial of a certain degree. One possible approach to this testing problem is to do a parametric polynomial fit and a nonparametric fit and to reject the null hypothesis of a polynomial function if the distance...

Analyzing functional data often leads to finding common factors, for which functional principal component analysis proves to be a useful tool to summarize and characterize the random variation in a function space. The representation in terms of eigenfunctions is optimal in the sense of L2...

Calculation of a confidence interval for intraclass correlation to assess inter-rater reliability is problematic when the number of raters is small and the rater effect is not negligible. Intervals produced by existing methods are uninformative: the lower bound is often close to zero, even in...