Vita e Pensiero

The present work aims to obtain the value of minimum sample size required by a good approximation by the normal curve for the sample mean difference. Particular care is given to what happens in the tails of the curves, with the aim of deriving confidence intervals for Gini’s mean difference. This goal is obtained by empirical methods and the presented results have an explorative nature. Simulation data have been obtained sampling from different distributions, considering symmetry versus asymmetry and the existence of the moments as main aspects in the underlying distribution. These remarks lead to the choice of the normal, the rectangular, the exponential and the Pareto distributions. All the obtained results indicate that the shape of the distribution from which the samples are generated is critically related to the minimum sample sizes required for a good approximation of the tails of the sample mean difference to the normal curve. Keywords: Gini Mean Difference, asymptotic distribution, convergence, U-statistic.

This special issue of Statistica & Applicazioni provides a selection of papers, which were presented and discussed at the International Conference to Honor Two Eminent Social Scientists.

The parameter estimation problem for the ARMA models is considered under a minimax approach: when the objective is to minimize the largest deviations in time series, it is useful to define a generalized version of a minimax estimator, minimizing the sum of the r-th powers of the k

In this paper, the bipolar mean, that can be seen as a frequencies distribution, has been extended to the discrete variables.

A procedure for the construction of an indicator in the presence of structured missing data is proposed.

A careful evaluation of university services is necessary. This evaluation is complex because it involves many aspects...

The aim of this paper is to propose a new operational measure for evaluating the degree of dependence existing between two nominal categorical variables.

Least square estimates of regression parameters may become unreliable when some outliers affect the data. This fact forces to search for different methods of estimation, some of which consist of substituting ranks to observations to avoid influences by extremes values.