Spanning trees and identifiability of a single-factor model

The aim of this paper is to propose conditions for exploring the class of identifiable Gaussian models with one latent variable. In particular, we focus attention on the topological structure of the complementary graph of the residuals. These conditions are mainly based on the presence of odd cycles and bridge edges in the complementary graph. We propose to use the spanning tree representation of the graph and the associated matrix of fundamental cycles. In this way it is possible to obtain an algorithm able to establish in advance whether modifying the graph corresponding to an identifiable model, the resulting graph still denotes identifiability.     

On the estimation of the Lorenz curve under complex sampling designs

Statistical Methods & Applications - This paper focuses on the estimation of the concentration curve of a finite population, when data are collected according to a complex sampling design with...     

Estimation of contingency tables in complex survey sampling using probabilistic expert systems

In this paper we explore the possibility to use a particular class of models, known as probabilistic expert systems, to define two classes of estimators of a contingency table in case of stratified sampling designs. The two classes are characterized by the different role of the sampling design: in the first, the sampling design is treated as an additional variable; in the second, it is used only for estimation purposes by means of the survey weights. The bias/variance trade off of these estimators is analyzed and the consequences of model misspecification are illustrated. Furthermore, it is shown that the Horvitz–Thompson estimator belongs to both classes of estimators. It comes out that the Horvitz–Thompson estimator is almost always inefficient but robust. Monte Carlo simulations illustrate the efficiency of the proposed estimators.     

Analysis and correction of bias in Total Decrease in Node Impurity measures for tree-based algorithms

Variable selection is one of the main problems faced by data mining and machine learning techniques. These techniques are often, more or less explicitly, based on some measure of variable importance. This paper considers Total Decrease in Node Impurity (TDNI) measures, a popular class of variable importance measures defined in the field of decision trees and tree-based ensemble methods, like Random Forests and Gradient Boosting Machines. In spite of their wide use, some measures of this class are known to be biased and some correction strategies have been proposed. The aim of this paper is twofold. Firstly, to investigate the source and the characteristics of bias in TDNI measures using the notions of informative and uninformative splits. Secondly, a bias-correction algorithm, recently proposed for the Gini measure in the context of classification, is extended to the entire class of TDNI measures and its performance is investigated in the regression framework using simulated and real data.     

A parametric model fitting time to first event for overdispersed data: application to time to relapse in multiple sclerosis

In this article, we propose a parametric model for the distribution of time to first event when events are overdispersed and can be properly fitted by a Negative Binomial distribution. This is a very common situation in medical statistics, when the occurrence of events is summarized as a count for each patient and the simple Poisson model is not adequate to account for overdispersion of data. In this situation, studying the time of occurrence of the first event can be of interest. From the Negative Binomial distribution of counts, we derive a new parametric model for time to first event and apply it to fit the distribution of time to first relapse in multiple sclerosis (MS). We develop the regression model with methods for covariate estimation. We show that, as the Negative Binomial model properly fits relapse counts data, this new model matches quite perfectly the distribution of time to first relapse, as tested in two large datasets of MS patients. Finally we compare its performance, when fitting time to first relapse in MS, with other models widely used in survival analysis (the semiparametric Cox model and the parametric exponential, Weibull, log-logistic and log-normal models).     

Changes in union status during the transition to parenthood in eleven European countries, 1970s to early 2000s

Couples who have children are increasingly likely to have lived together without being married at some point in their relationship. Some couples begin their unions with cohabitation and marry before first conception, some marry during pregnancy or directly after the first birth, while others remain unmarried 3 years after the first birth. Using union and fertility histories since the 1970s for eleven countries, we examine whether women who have children in unions marry, and if so, at what stage in family formation. We also examine whether women who conceive when cohabiting are more likely to marry or separate. We find that patterns of union formation and childbearing develop along different trajectories across countries. In all countries, however, less than 40 per cent of women remained in cohabitation up to 3 years after the first birth, suggesting that marriage remains the predominant institution for raising children.     

Here or There? Shifting Meanings and Practices in Mother–Child Relationships across Time and Space

In this paper, I analyse the changes that mothers and children experience in their relationship due to the physical separations and reunions entailed by the international migration process. I argue that the different geographical configurations that migrant families take over time are the outcome of a negotiation of care responsibility and desired geographies of family life, and are accompanied by changing meanings and practices in intimate relationships: the location of care relationships is influenced by the relatives' capacity both to take part in family negotiations as well as to overcome the constraints imposed by policies. Time is relevant because it leads to shifting meanings and practices of transnational family life, as well as to the changing role of children in the family.     

Looking for the Best and Brightest? Deservingness Regimes in Italian Labour Migration Management

It is often maintained that contemporary foreign labour recruitment programs have taken an increasingly selective stance and that skills are increasingly crucial in granting migrants easier access and stronger welfare and residency rights in receiving countries. The paper provides a retrospective account of the evolution of Italian labour migration management, paying attention to the changing selection criteria reflected in some of its “front, back and side doors”. It will be shown that Italian labour migration management is increasingly embracing a bifurcated regime of deservingness. Despite some recent tentative signs of change towards an increased evaluation of high skills as a selection principle embedded into a competitiveness‐driven frame, over the last decades labour migration management has been largely dominated by the recruitment of a low‐skilled workforce. The paper discusses the emergence of a specific construction migrants’ deservingness placing Italy in a peculiar position in a European context.     

Can antenatal classes reduce the rate of cesarean section in southern Italy?

Background

Among European Countries, Italy has the highest rate of cesarean section (36.8%), and in the Campania region this rate reaches 60.0%.