Bruno Bertaccini

Robust ANalysis Of VAriance: An approach based on the Forward Search

A simple robust method for the detection of atypical observations and the analysis of their effect in the ANOVA framework is presented. It is proposed to use a forward search procedure that orders the observations by their closeness to the hypothesized model. The procedure can be applied following two different strategies: one that adds units maintaining the relative group dimension and the other that adds only one new unit at each step of the search. The assessment of the goodness of the method is carried out through a simulation study. The method is then applied to real data. Results are presented through easy to interpret plots which are powerful in revealing the structure of the data.

Quality Assessment of the University Educational Process: an Application of the ECSI Model

In a university, students represent the final users as well as the principal actors of the formative services. A measure of their perceived quality is essential for planning changes that would increase the level of the quality of these services. This perceived quality is analysed in this paper with the ECSI (European Customer Satisfaction Index) methodology. The ECSI, which implements a structural equation model, is aimed to represent the satisfaction of the students with some latent variables gauged through a set of observable indicators. We extend the ECSI to the data obtained from graduates of the University of Florence employed one year after graduation.

Long-term Outcomes of Pediatric-Onset Hypertrophic Cardiomyopathy and Age-Specific Risk Factors for Lethal Arrhythmic Events

Importance Predictors of lethal arrhythmic events (LAEs) after a pediatric diagnosis of hypertrophic cardiomyopathy (HCM) are unresolved. Existing algorithms for risk stratification are limited to patients older than 16 years because of a lack of data on younger individuals. Objective To describe the long-term outcome of pediatric-onset HCM and identify age-specific arrhythmic risk factors. Design, Setting, and Participants This study assessed patients with pediatric-onset hypertrophic cardiomyopathy diagnosed from 1974 to 2016 in 2 national referral centers for cardiomyopathies in Florence, Italy. Patients with metabolic and syndromic disease were excluded. Exposures Patients were assessed at 1-year intervals, or more often, if their clinical condition required. Main Outcomes and Measures Lethal arrhythmic events (LAEs) and death related to heart failure. Results Of 1644 patients with HCM, 100 (6.1%) were 1 to 16 years old at diagnosis (median [interquartile range], 12.2 [7.3-14.1] years). Of these, 63 (63.0%) were boys. Forty-two of the 100 patients (42.0%) were symptomatic (defined as an New York Heart Association classification higher than 1 or a Ross score greater than 2). The yield of sarcomere gene testing was 55 of 70 patients (79%). During a median of 9.2 years during which a mean of 1229 patients were treated per year, 24 of 100 patients (24.0%) experienced cardiac events (1.9% per year), including 19 LAEs and 5 heart failure–related events (3 deaths and 2 heart transplants). Lethal arrhythmic events occurred at a mean (SD) age of 23.1 (11.5) years. Two survivors of LAEs with symptoms of heart failure experienced recurrent cardiac arrest despite an implantable cardioverter defibrillator. Risk of LAE was associated with symptoms at onset (hazard ratio [HR], 8.2; 95% CI, 1.5-68.4; P = .02) and Troponin I or Troponin T gene mutations (HR, 4.1; 95% CI, 0.9-36.5; P = .06). Adult HCM risk predictors performed poorly in this population. Data analysis occurred from December 2016 to October 2017. Conclusions and Relevance Pediatric-onset HCM is rare and associated with adverse outcomes driven mainly by arrhythmic events. Risk extends well beyond adolescence, which calls for unchanged clinical surveillance into adulthood. In this study, predictors of adverse outcomes differ from those of adult populations with HCM. In secondary prevention, the implantable cardioverter defibrillator did not confer absolute protection in the presence of limiting symptoms of heart failure.

Robust diagnostics in university performance studies

The presence of anomalous observations (outliers) in a set of data is one of the greatest problems in methodological statistics, one that scientists were already aware of many years ago, as can be seen in the comments made by the American astronomer Peirce1 over 150 years ago.

Evaluating the University Educational Process. A Robust Approach to the Drop-out Problem

The use of robust procedures in regression model estimation identifies outlier data that can inform on specific subpopulations. The aim of this study is to analyse the problem of first year dropouts at the University of Florence. A set of administrative data, collected at the moment of enrolment, combined with the information gathered through a specific survey of the students enrolled in the 2001–2002 academic year at the same athenaeum, was used for the purpose. In order to identify the most important variables affecting the students’ dropout, the data were first fitted with generalized linear models estimated with classical methods. The same models were then estimated with robust methods that allowed the detection of groups of outliers. These in turn were analysed to determine the personal or contextual characteristics. These results may be relevant for the implementation of academic policy changes.

Robust Transformation of Proportions Using the Forward Search

The aim of this work is to detect the best transformation parameters to normality when data are proportions. To this purpose we extend the forward search algorithm introduced by Atkinson and Riani (2000), and Atkinson et al. (2004) to the transformation proposed by Aranda-Ordaz (1981). The procedure, implemented by authors with R package, is applied to the analysis of a particular characteristic of Tuscany industries. The data used derive from the Italian industrial census conducted in the year 2001 by the Italian National Statistical Institute (ISTAT).

Santa Maria del Fiore Dome Behavior: Statistical Models for Monitoring Stability

The article describes the work in progress for analysis of the behavior of the web cracks on Brunelleschi’s Dome of Santa Maria del Fiore. The web cracks in the Dome have always given rise to concern about the stability of the monument. The analyses performed show a slight increase in the size of the main cracks and, at the same time, a relationship with the environmental variables. None of the studies conducted in the past simultaneously involved all the variables detected by the monitoring system. Understanding the relationships among all the endogenous and exogenous variables characterizing the phenomenon under study can help to predict the static evolution of the monument and to automatically detect any atypical behavior, the identification of which would, of course, be of paramount importance for the preservation of the monument, and, more in general, for the preservation of the cultural heritage.

Robust Random Effects Models: A Diagnostic Approach Based on the Forward Search

This paper presents a robust procedure for the detection of atypical observations and for the analysis of their effect on model inference in random effects models. Given that the observations can be outlying at different levels of the analysis, we focus on the evaluation of the effect of both first and second level outliers and, in particular, on their effect on the higher level variance which is statistically evaluated with the Likelihood-Ratio Test. A cut-off point separating the outliers from the other observations is identified through a graphical analysis of the information collected at each step of the Forward Search procedure; the Robust Forward LRT is the value of the classical LRT statistic at the cut-off point.

The Effectiveness of University Education: A Structural Equation Model

The evaluation of the effectiveness of higher education is a crucial aspect of competitiveness of modern economies. In this contribution we investigate the quality and effectiveness of higher education in Italy using a structural equation model; in particular, we evaluate the performance of the university system from the users’ point of view, both immediately following (internal effectiveness), and one year after (external effectiveness), the completion of the degree. The model allows the construction of synthetic indexes and hence the ranking of study programs.

Robust Fuzzy Classification

One of the most important problems among the methodological issues discussed in cluster analysis is the identification of the correct number of clusters and the correct allocation of units to their natural clusters. The most widely used index to determine the optimal number of groups is the Calinski Harabasz index. As shown in this paper, the presence of atypical observations has a strong effect on this index and may lead to the determination of a wrong number of groups. Furthermore, in order to study the degree of belonging of each unit to each group it is standard practice to apply a fuzzy k-means algorithm. In this paper we tackle this problem using a robust and efficient approach based on a forward search algorithm. The method is applied on a data set containing performance evaluation indicators of Italian universities.