We discuss a variety of clustering problems arising in combinational applications and in classifying objects into homogenous groups. For each problem we discuss solution strategies that work well in practice. We also discuss the importance of careful modelling in clustering problems. Citation...

Often, in finite samples, the true level of the confidence intervals for natural estimators of inequality indices belonging to the Gini family differs greatly from their nominal level, which is based on the asymptotic confidence limits. This paper shows how the Gram-Charlier series can be used...

We discuss two experimental designs and show how to use them to evaluate difficult empirical combinatorial problems. We restrict our analysis here to the knapsack problem but comment more generally on the use of computational testing to analyze the performances of algorithms. Citation Copyright...

In this paper we present some nonparametric bootstrap methods to construct distribution-free confidence intervals for inequality indices belonging to the Gini family. These methods have a coverage accuracy better than that obtained with the asymptotic distribution of their natural estimators,...