STATISTICA & APPLICAZIONI
Six-monthly journal aimed at promoting research in the Methodological Statistics field
Statistica & Applicazioni is a six-monthly journal aimed at promoting research in statistical methodology and its original and innovative applications. Statistica & Applicazioni publishes research articles (and short notes) on theoretical, computational and applied statistics.
The journal is Open Access.
The journal was founded in 2003 by the following Departments belonging to different Italian Universities:
- Quantitative Methods - University of Brescia;
- Quantitative Methods for Business and Economic Sciences - University of Milano-Bicocca;
- Statistics - University of Milano-Bicocca;
- Information Technology and Mathematical Methods - University of Bergamo;
- Economics and Statistics - University of Calabria;
- «Silvio Vianelli» Mathematical and Statistical Sciences - University of Palermo;
- Statistics - Catholic University of the Sacred Heart, Milan.
At present the journal is supported by the following organizations:
- DMS StatLab - University of Brescia
- Department of Statistics and Quantitative Methods - University of Milano-Bicocca
- Department of Statistics - Catholic University of the Sacred Heart, Milan
- Department of Economics, Statistics and Finance - University of Calabria
- Department of Engineering - University of Bergamo
- Az.Agr.Case Basse of Gianfranco Soldera
- Indexed in: Current Index to Statistics - Ulrich's Periodicals Directory
- Available on: Torrossa - EBSCO Discovery Service
In this issue
The current paper aims to present the method of Non-Symmetrical Correspondence Analysis (NSCA) based on Emerson’s orthogonal polynomials, which takes into account, in efficient way, the ordinal structure of the data. The extension of NSCA is the so called Singly Ordered Non-Symmetrical Correspondence Analysis version (SONSCA), that, by taking into account the ordinal structure in the table, improving the interpretation ability of the analysis. The methods was applied to 40 in-depth interviews, gathered with people in treatment for their problems with addiction. NSCA and SONSCA are used to evaluate if the classes of age of the subjects interviewed influence the addiction thematic categories that characterize their discourses.The work provides insight on NSCA and SONSCA methods and how they could be applied in the psychological context and in particular to study the dependence between ordinal and categorical variables reported in a contingency table.
Spline functions are a very flexible and powerful tool, which has been adopted in several applied fields, such as the evaluation of Public Services. In this paper, we use splines to model membership and non-membership functions in the context of Intuitionistic Fuzzy Sets. Such choice allows us to properly consider, in a very general way, the degree of satisfaction, dissatisfaction, as well as the underlying uncertainty expressed by participants rating evaluation questionnaires. This is a compelling advantage of the fuzzy analysis as compared to the use of simple descriptive statistics. We apply this approach using the questionnaires prepared by the ANVUR to assess University courses, and we perform a sensitivity analysis to validate the results.
This article deals with the cluster analysis of panels of short time series which are gathered by observing a fixed set of units at regular intervals of time. Given the reduced length of time series, we follow a non-model-based strategy in which the priority is to assign a value to the dissimilarity between the observed time series themselves rather than to the processes that generate those time series. Our main contribution is a new method for clustering short panel data that can be used to explore whether the various cross-sectional time series move in a similar way and whether there is substantial variation in each panel over time. It is also important to identify outliers and other anomalies because researchers may be especially interested in studying panels that behave unusually, at least with respect to the rest of the data.Although finding group structures within short panels remains challenging, the results acquired for real and simulated data are valid and encouraging for further study.
In recent years many important events have affected the EU political and economic stage: a new common currency, enlargements occurred in three waves, a huge economic crisis and a country’s request to exit the EU, just to cite a few. These events have deeply contributed to change the citizens’ attitude towards the EU and the perception of the influence of the EU on their quality of life. The present work aims at analyzing if the EU is seen positively or negatively by its citizens with regard to their quality of life. This analysis is carried out in two-steps via (i) a nonlinear principal component analysis to extract an indicator measuring citizens’ evaluation about how much the EU influences their quality of life, and (ii) a multilevel model to take into account some individual or country-specific factors affecting this perception. Specific attention is given to national differences.