In this paper, we propose a new test of uniformity on the circle based on the Gini mean difference of the sample arc-lengths, i.e. the gaps between successive observations on the circumference of the circle. These sample arc-lengths are analogous to sample spacings, which are the gaps between successive observations on the real line. Such a Gini mean difference test is analogous to Rao’s spacin...

Small area estimation has received enormous attention in recent years due to its wide range of application, particularly in policy making decisions. The variance based on direct sample size of small area estimator is unduly large and there is a need of constructing model based estimator with low mean squared prediction error (MSPE). Estimation of MSPE and in particular the bias correction of MS...

In this paper, we propose a new test of uniformity on the circle based on the Gini mean difference of the sample arc-lengths. These sample arc-lengths, which are the gaps between successive observations on the circumference of the circle, are analogous to sample spacings on the real line. The Gini mean difference, which compares these arc-lengths between themselves, is analogous to Rao’s spacin...

Graphs often occur as ”natural” representations of structured objects in different application areas of machine learning. To adopt methods like central clustering or principal component analysis for graphs, an understanding of the structural version of the sample mean is imperative. We present an analytic and geometric view of the structural mean.