The Kendall (1955) rank correlation coefficient evaluates the degree of similarity between two sets of ranks given to a same set of objects. This coefficient depends upon the number of inversions of pairs of objects which would be needed to transform one rank order into the other. In order to do so, each rank order is represented by the set of all pairs of objects (e.g., [a,b] and [b,a] are the...

Climatic parameters in time and space scales of change are for many reasons of Changes and how they should be based on observations using a statistical method to be determined. Analysis of the most widely used statistical methods that assess potential climate change on hydrological time series, such series of precipitation, temperature and flow rate used. This study of 11 synoptic,rain gage and...

Comparing and ranking information is an important topic in social and information sciences, and in particular on the web. Its objective is to measure the difference of the preferences of voters on a set of candidates and to compute a consensus ranking. Commonly, each voter provides a total order of all candidates. In this work we consider the generalization of total orders and bucket orders to ...

The Mann-Kendall test is a commonly-used nonparametric test for time trend. However, the standard P-values obtained from it are based on an assumption of independence between observations (since the theory is that of the Kendall correlation). However, observations in time series are often autocorrelated: knowing that one observation is larger than the mean may tell you that the next observation...

The development of new vessels on the retina of people with diabetes is rare, but is likely to lead to severe visual impairment. This paper investigates the selection of suitable image features for the automatic detection of new vessels on the optic disc. The features are chosen based on their discrimination capability (tested using the non-parametric Wilcoxon rank sum and Ansari-Bradley disper...

A unifying framework to test for causal effects in non-linear models is proposed. We consider a generalized linear-index regression model with endogenous regressors and no parametric assumptions on the error disturbances. To test the significance of the effect of an endogenous regressor, we propose a test statistic that is a kernel-weighted version of the rank correlation statistic (tau) of Ken...