Vita e Pensiero

In modern socio-economic systems, often the aim of a performance analysis or quality evaluation is to compare different products, different manufacturing plants or service centres, different actions or distinct treatments. The question is, ‘‘Which is better?’’ This is complicated because the considered aspects are often measured through categorical data and the results can be affected by confounding factors. To solve this problem we discuss some directional permutation tests based on the nonparametric combination of dependent permutation tests (NPC) for two-sample comparisons in the presence of ordinal categorical variables and confounding factors. In particular we present a new permutation test based on the combination of a finite number of sample moments. To reduce the confounding effects we consider the joint application of stratification and the NPC method. We also show the results of Monte Carlo simulations in order to compare permutation solutions with other nonparametric tests and to evaluate the robustness of the test based on moments.

We propose a subgroups decomposition of the uniformity index recently introduced by Zenga [2007]. The decomposition scheme adopted follows the structure of the index which is based on the ratios between lower and upper arithmetic means. The keypoint is the evaluation of the point uniformity index both within the same subgroup and between two different subgroups. The decomposition obtained for the uniformity index is finally applied to achieve an analogous decomposition of the inequality index.

By using the conditions that the expected value of an absolute random variable X is finite and positive and that the point inequality measure I ðpÞ is uniform for 0 < p < 1, this paper discusses the question of the existence of such random variable and proves that this problem has a unique solution. The obtained cumulative distribution function of X is a truncated Pareto distribution, with traditional inequality parameter equal to 0,5 and with support depending on the finite and positive expected value and the level of uniformity, based on the ratios between the lower means and the upper means, used for defining the point inequality measure I(p).

In this paper we derive Bayesian and non-Bayesian estimators for the parameter of the power function distribution, and prediction intervals for the maximum of a future sample. We apply our approach to field data of plant species relative abundance, the abundance of a given species divided by the total abundance of all plant species in given a community, collected in a biodiversity project in central Europe.

This paper aims at proposing a new methodology to compute recovery rate on non-performing bank loans, in order to confine this variable within the interval [0,1]. Such a methodology is then applied to data on loans gathered by the Bank of Italy and some interesting characteristics of the loan recovery process in the Italian banking market are highlighted. The combined effects of some variables on the recovery rates are also analysed. In particular, the presence of either collateral or personal guarantee, the borrower’s residence area are considered, thereby emphasizing the relationship between the recovery rate and the total exposure.

STATISTICA & APPLICAZIONI, VOL. IV, Numero Speciale 2, 2006 We regret a mispelling error of the author name Kadarmanto on the cover page, table of contents and Editorial (page 3). Our apologies to the author.