L'esperienza della VQR 2011-2014 nell'area delle Scienze Economiche e Statistiche:la situazione nei macrosettori Economico Aziendale e Statistico

La prima parte del mio intervento, corredata da grafici e da tabelle, riguarda non soltanto le aree dei non bibliometrici, ma la valutazione in generale. Come è noto ogni prodotto scientifico è stato valutato secondi i seguenti parametri qualitativo-numerici: eccellente (1), elevato (0,7), discreto (0,4), accettabile (0,1), limitato (0). Da tutte le valutazioni espresse dai vari GEV per i prodotti conferiti dalle aree scientifiche, è stato ricavato, per ognuno dei Settori Disciplinari, un punteggio medio che tiene conto dei risultati a livello nazionale

Extending Functional kriging to a multivariate context

Environmental data usually have a spatio-temporal structure; pollutant concentrations, for example, are recorded along time and space. Generalized Additive Models (GAMs) represent a suitable tool to model spatial and/or temporal trends of this kind of data, that can be treated as functional, although they are collected as discrete observations. Frequently, the attention is focused on the prediction of a single pollutant at an unmonitored site and, at this aim, we extend kriging for functional data to a multivariate context by exploiting the correlation with the other pollutants. In particular, we propose two procedures: the first one (FKED) combines the regression of a variable (pollutant), of primary interest on the other variables, with functional kriging of the regression residuals; the second one (FCK) is based on linear unbiased prediction of spatially correlated multivariate random processes. The performance of the two proposed procedures is assessed by cross validation; data recorded during a year (2011) from the monitoring network of the state of California (USA) are considered

Element weighted Kemeny distance for ranking data

Preference data are a particular type of ranking data that arise when several individuals express their preferences over a finite set of items. Within this framework, the main issue concerns the aggregation of the preferences to identify a compromise or a “consensus”, defined as the closest ranking (i.e. with the minimum distance or maximum correlation) to the whole set of preferences. Many approaches have been proposed, but they are not sensitive to the importance of items: i.e. changing the rank of a highly-relevant element should result in a higher penalty than changing the rank of a negligible one. The goal of this paper is to investigate the consensus between rankings taking into account the importance of items (element weights). For this purpose, we present: i) an element weighted rank correlation coefficient as an extension of the Emond and Mason’s one, and ii) an element weighted rank distance as an extension of the Kemeny distance. The one-to-one correspondence between the weighted distance and the rank correlation coefficient is analytically proved. Moreover, a procedure to obtain the consensus ranking among several individuals is described and its performance is studied both by simulation and by the application to real datasets

Causal Models for Monitoring University Ordinary Financing Fund

Recently iterated decreasing government transfers and an increasing proportion of budget allotted basing on competitive performances, took Italian Universities started struggling with competition for funds, in particular for the University Ordinary Financing Fund (FFO). Aim of this paper is monitoring variables responsible for FFO indicators, where monitoring means: describing, analysing retrospectively, predicting and intervening on variables responsible for indicators. All this aims can be achieved by statistical techniques that should be theoretically equipped with the distinction between predicting under observation and predicting under intervention, in order to provide correct answers to the distinct tasks of pure out of sample extrapolation and policy making

GAMs and functional kriging for air quality data

Data having spatio-temporal structure are often observed in environmental sciences. They may be considered as discrete observations from curves along time and/or space and treated as functional. Generalized Additive Models (GAMs) represent a useful tool for modelling, for example, as pollutant concentrations describing their spatial and/or temporal trends.Usually, the prediction of a curve at an unmonitored site is necessary and, with this aim, we extend kriging for functional data to a multivariate context. Moreover, even if we are interested only in predicting a single pollutant, such as PM10, the estimation can be improved exploiting its correlation with the other pollutants. Cross validation is used to test the performance of the proposed procedure

Ranking coherence in Topic Models using Statistically Validated Networks

Probabilistic topic models have become one of the most widespread machine learning techniques in textual analysis. Topic discovering is an unsupervised process that does not guarantee the interpretability of its output. Hence, the automatic evaluation of topic coherence has attracted the interest of many researchers over the last decade, and it is an open research area. The present article offers a new quality evaluation method based on Statistically Validated Networks (SVNs). The proposed probabilistic approach consists of representing each topic as a weighted network of its most probable words. The presence of a link between each pair of words is assessed by statistically validating their co-occurrence in sentences against the null hypothesis of random co-occurrence. The proposed method allows one to distinguish between high-quality and low-quality topics, by making use of a battery of statistical tests. The statistically significant pairwise associations of words represented by the links in the SVN might reasonably be expected to be strictly related to the semantic coherence and interpretability of a topic. Therefore, the more connected the network, the more coherent the topic in question. We demonstrate the effectiveness of the method through an analysis of a real text corpus, which shows that the proposed measure is more correlated with human judgement than the state-of-the-art coherence measures

Element weighted Kemeny distance for ranking data

Preference data are a particular type of ranking data that arise when n individuals express their preferences over a finite set of items. Within this framework, the main issue concerns the aggregation of the preferences to identify a compromise or a “consensus”, defined as the closest ranking (i.e. with the minimum distance or maximum correlation) to the whole set of preferences.  Many approaches have been proposed, but they are not sensitive to the importance of items: i.e.  changing the rank of a highly-relevant element should result in a higher penalty than changing the rank of a negligible one. The goal of this paper is to investigate the consensus between rankings taking into account the importance of items (element weights).  For this purpose, we present:  i) an element weighted rank correlation coefficient tau_ew as an extension of the Emond and Mason’s tau, and ii) an element weighted rank distance d_ew as an extension of the Kemeny distance d. The one-to-one correspondence between the weighted distance and the rank correlation coefficient is analytically proved. Moreover, a procedure to obtain the consensus ranking among n individuals is described and its performance is studied both by simulation and by the application to real datasets

Clustering alternatives in preference-approvals via novel pseudometrics

Preference-approval structures combine preference rankings and approval voting for declaring opinions over a set of alternatives. In this paper, we propose a new procedure for clustering alternatives in order to reduce the complexity of the preferenceapproval space and provide a more accessible interpretation of data. To that end, we present a new family of pseudometrics on the set of alternatives that take into account voters’ preferences via preference-approvals. To obtain clusters, we use the Ranked k-medoids (RKM) partitioning algorithm, which takes as input the similarities between pairs of alternatives based on the proposed pseudometrics. Finally, using non-metric multidimensional scaling, clusters are represented in 2-dimensional space

Nitrogen uptake and nitrogen fertilizer recovery in old and modern wheat genotypes grown in the presence or absence of interspecific competition

Choosing genotypes with a high capacity for taking up nitrogen (N) from the soil and the ability to efficiently compete with weeds for this nutrient is essential to increasing the sustainability of cropping systems that are less dependent on auxiliary inputs. This research aimed to verify whether differences exist in N uptake and N fertilizer recovery capacity among wheat genotypes and, if so, whether these differences are related to a different competitive ability against weeds of wheat genotypes. To this end, 12 genotypes, varying widely in morphological traits and year of release, were grown in the presence or absence of interspecific competition (using Avena sativa L. as a surrogate weed). Isotopic tracer 15N was used to measure the fertilizer N uptake efficiencies of the wheat genotypes and weed. A field experiment, a split-plot design with four replications, was conducted during two consecutive growing seasons in a typical Mediterranean environment. In the absence of interspecific competition, few differences in either total N uptake (range: 98–112 kg N ha–1) or the 15N fertilizer recovery fraction (range: 30.0–36.7%) were observed among the wheat genotypes. The presence of competition, compared to competitor-free conditions, resulted in reductions in grain yield (49%), total N uptake (29%), and an 15N fertilizer recovery fraction (32%) that were on average markedly higher in modern varieties than in old ones. Both biomass and grain reductions were strongly related to the biomass of the competitor (correlation coefficients > 0.95), which ranged from 135 g m–2 to 573 g m–2. Variations in both grain and biomass yield due to interspecific competition were significantly correlated with percentage of soil cover and leaf area at tillering, plant height at heading, and total N uptake, thus highlighting that the ability to take up N from the soil played a certain role in determining the different competitive abilities against weed of the genotypes