A STRUCTURAL MODEL OF TURNOUT AND VOTING IN MULTIPLE ELECTIONS

This paper develops a unified approach to study participation and voting in multiple elections. The theoretical setting combines an uncertain‐voter model of turnout with a spatial model of voting behavior. We apply our framework to the study of turnout and voting in US Presidential and Congressional elections. We structurally estimate the model using individual‐level data for the 2000 elections, and quantify the relationships between observed individual characteristics and unobserved citizens’ ideological preferences, information, and civic duty. We then use the estimated model, which replicates the patterns of abstention, selective abstention, split‐ticket voting, and straight‐ticket voting observed in the data, to assess the effects of policies that may increase citizens’ information and sense of civic duty on their turnout and voting behavior.     

Missing consequences in multiattribute utility theory

This paper addresses how to deal with decision alternatives with missing consequences in multicriteria decision-making problems. We propose disregarding the attributes for which a decision alternative provides no consequence by redistributing their respective weights throughout the objective hierarchy in favor of a straightforward idea: the assignation of the respective attribute range as a default value for missing consequences due to possible uncertainty about the decision alternative consequences. In both cases, decision alternatives are evaluated by means of an additive multi-attribute utility model. An illustrative example of the restoration of radionuclide contaminated aquatic ecosystems is shown.     

Objective Bayesian analysis for the multivariate skew-t model

We propose a novel Bayesian analysis of the p-variate skew-t model, providing a new parameterization, a set of non-informative priors and a sampler specifically designed to explore the posterior density of the model parameters. Extensions, such as the multivariate regression model with skewed errors and the stochastic frontiers model, are easily accommodated. A novelty introduced in the paper is given by the extension of the bivariate skew-normal model given in Liseo and Parisi (2013) to a more realistic p-variate skew-t model. We also introduce the R package mvst, which produces a posterior sample for the parameters of a multivariate skew-t model.     

On the estimation of the influence curve

We prove the asymptotic validity of bootstrap confidence bands for the influence curve from its usual estimator (the sensitivity curve). The proof is based on the use of Gill's generalized delta method for Hadamard differentiable operators. Some statistical applications, in particular to the estimation of asymptotic variance, are given.     

Sampling hierarchies of discrete random structures

Statistics and Computing - Hierarchical normalized discrete random measures identify a general class of priors that is suited to flexibly learn how the distribution of a response variable changes...     

Flexible clustering via hidden hierarchical Dirichlet priors

The Bayesian approach to inference stands out for naturally allowing borrowing information across heterogeneous populations, with different samples possibly sharing the same distribution. A popular Bayesian nonparametric model for clustering probability distributions is the nested Dirichlet process, which however has the drawback of grouping distributions in a single cluster when ties are observed across samples. With the goal of achieving a flexible and effective clustering method for both samples and observations, we investigate a nonparametric prior that arises as the composition of two different discrete random structures and derive a closed-form expression for the induced distribution of the random partition, the fundamental tool regulating the clustering behavior of the model. On the one hand, this allows to gain a deeper insight into the theoretical properties of the model and, on the other hand, it yields an MCMC algorithm for evaluating Bayesian inferences of interest. Moreover, we single out limitations of this algorithm when working with more than two populations and, consequently, devise an alternative more efficient sampling scheme, which as a by-product, allows testing homogeneity between different populations. Finally, we perform a comparison with the nested Dirichlet process and provide illustrative examples of both synthetic and real data.     

Population Changes in Europe. Demographic and Social Prospects and Problems

Abstract. Over recent years, the European Community has experienced profound demographic changes which, to a large extent, have already determined its demographic future. One of the more significant consequences of demographic and social trends is a marked fragmentation of the family with people giving absolute priority to the individual dimension in their lives and in terms of society as a whole. Legal regulations and administrative practice have not totally–and not everywhere – adapted to the far-reaching changes in the family so as to adequately protect the family and its weaker members in particular: children, the elderly, the disabled, people with mental and social illnesses, the poor. In this field, some measures will be proposed in this paper. The imbalances in demographic-occupational growth in the Euro-Mediterranean area–the area with which the EC is most directly in contact–are and will be of an exceptional amplitude, as has already been shown, to an extent never before experienced in the history of mankind. We must ask ourselves if this very strong differential pressure, which is also of a political and socio-economic nature, will lead to steady and long-lasting migratory flows. Some considerations regarding the future action and intervention of the EC close the paper.