Extreme ranking analysis in robust ordinal regression

We extend the principle of robust ordinal regression with an analysis of extreme ranking results. In our proposal, we consider the whole set of instances of a preference model that is compatible with preference information provided by the DM. We refer to both, the well-known UTA^GMS method, which builds the set of general additive value functions compatible with DM's preferences, and newly introduced in this paper PROMETHEE^GKS, which constructs the set of compatible outranking models via robust ordinal regression. Then, we consider all complete rankings that follow the use of the compatible preference models, and we determine the best and the worst attained ranks for each alternative. In this way, we are able to assess its position in an overall ranking, and not only in terms of pairwise comparisons, as it is the case in original robust ordinal regression methods. Additionally, we analyze the ranges of possible comprehensive scores (values or net outranking flows). We also discuss extensions of the presented approach on other multiple criteria problems than ranking. Finally, we show how the presented methodology can be applied in practical decision support, reporting results of three illustrative studies.     

Beyond Markowitz with multiple criteria decision aiding

The paper is about portfolio selection in a non-Markowitz way, involving uncertainty modeling in terms of a series of meaningful quantiles of probabilistic distributions. Considering the quantiles as evaluation criteria of the portfolios leads to a multiobjective optimization problem which needs to be solved using a Multiple Criteria Decision Aiding (MCDA) method. The primary method we propose for solving this problem is an Interactive Multiobjective Optimization (IMO) method based on so-called Dominance-based Rough Set Approach (DRSA). IMO-DRSA is composed of two phases: computation phase, and dialogue phase. In the computation phase, a sample of feasible portfolio solutions is calculated and presented to the Decision Maker (DM). In the dialogue phase, the DM indicates portfolio solutions which are relatively attractive in a given sample; this binary classification of sample portfolios into ‘good’ and ‘others’ is an input preference information to be analyzed using DRSA; DRSA is producing decision rules relating conditions on particular quantiles with the qualification of supporting portfolios as ‘good’; a rule that best fits the current DM’s preferences is chosen to constrain the previous multiobjective optimization in order to compute a new sample in the next computation phase; in this way, the computation phase yields a new sample including better portfolios, and the procedure loops a necessary number of times to end with the most preferred portfolio. We compare IMO-DRSA with two representative MCDA methods based on traditional preference models: value function (UTA method) and outranking relation (ELECTRE IS method). The comparison, which is of methodological nature, is illustrated by a didactic example.     

A comparison between the simulated annealing and the EM algorithms in normal mixture decompositions

We compare the performances of the simulated annealing and the EM algorithms in problems of decomposition of normal mixtures according to the likelihood approach. In this case the likelihood function has multiple maxima and singularities, and we consider a suitable reformulation of the problem which yields an optimization problem having a global solution and at least a smaller number of spurious maxima. The results are compared considering some distance measures between the estimated distributions and the true ones. No overwhelming superiority of either method has been demonstrated, though in one of our cases simulated annealing achieved better results.     

Gender disparities in the Italian regions from a human development perspective

The aim of this paper is to analyse, which is the current situation of gender disparities in Italy and how such disparities are distributed among Italian regions. In order to quantify such disparities a comprehensive framework for assessment is required by using a human development approach. A gender-oriented investigation of effective available capabilities for men and women in the Italian regions reveals that gender inequality in Italy seems to be a persistent phenomenon. Finally, the gender disparities in the empowerment dimension and in the other social dimensions seem to be mutually reinforcing with the economic dimensions, especially in the labour market.     

Standardized Income Inequality Data for Use in Cross-National Research

This article introduces the first version of a new, standardized data tool that can be used to test models of global income allocation, the Standardized Income Distribution Database (SIDD). It is based on a comprehensive collection of income distribution data compiled by the United Nations University's World Institute for Development Economics Research (UNU-WIDER 2000 ). International and intertemporal inconsistencies in these data have historically limited its use. We estimate adjustment factors for different scopes of coverage, income definitions, and reference units which, when applied to the raw data, bring all data to a common standard based on national coverage, gross income, and household per capita inequality. Criterion validity checks confirm that these adjustments boost the correlation between measured income inequality and national social indicators. The SIDD is also clean, free of duplicates, and easy to access. The SIDD will be useful both to students reading income inequality and to those conducting broad cross-national research on the relationship between income inequality and a range of important outcomes, such as health, criminality, and social support.     

Sexual Prejudice in Sport Scale: A New Measure

ABSTRACT

This study describes the process of developing and validating the Sexual Prejudice in Sport Scale (SPSS), which is a multidimensional instrument developed to assess attitudes toward lesbians and gay men (LG) in sports. The authors conducted two studies: first, to establish the factor structure of the SPSS on 297 heterosexual athletes; and, second, to test the reliability and validity of the resulting 19-item scale on a sample of 311 heterosexual and 160 LG athletes. Exploratory factor analysis of an initial item pool yielded three factors: open rejection, which assesses the blatant prejudice expressed toward LG people; denial of visibility, which evaluates attitude toward the coming out of LG people; and gendering performance, which corresponds to gender stereotypes about performance/skills of LG people. A confirmatory factor analysis was conducted to assess the stability of the SPSS. The authors documented internal consistency, test-retest stability, and convergent/divergent validity. Implications and directions for future research are discussed.