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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
The journal is


ISSN carta: 18246672

In this issue


by Eugenio Brentari pages: 1
Fuzzy methods for the analysis of psychometric data: an application for measuring reading disability
by Isabella Morlini pages: 13 Download
Psychometrics should ideally measure multidimensional concepts like skills, knowledge, abilities, attitudes, personality traits and educational achievement, which cannot be captured by a single variable. In this paper, we suggest a method based on the fuzzy set theory for the construction of a fuzzy synthetic index of the latent psychometric phenomenon, using the set of variables obtained with instruments such as questionnaires or tests. Criteria for assigning values to the membership function as well as criteria for defining the weights of the variables are discussed. For discrete variables, we use a fuzzy quantification method based on the sampling cumulative function. An application regarding the measurement of reading disability in students attending elementary and middle school in Italy is presented.
On fitting measurement error model using sample entropy moments
by Amjad D. Al-Nasser, Midhat M. Eidous pages: 11 Download
In this article, we suggest using the sample entropy to fit the structural measurement error model. Using sample entropy in data analysis can be considered as a data screening to avoid problem in the data such as heteroscedasticity and co-linearity. The measurement error model is fitted under the assumption that the intercept is known. The mathematical derivation of the slope and its properties are given. An illustration using real data analysis to fit the relationship between happiness rate and human development index is given. The data analysis showed that there is a positive effect of human development index on happiness rate.
Joint decomposition by subpopulations and sources of the point and synthetic Gini indexes
by Igor Valli, Michele M. Zenga pages: 49 Download
The decompositions proposed in this paper are applied to the net disposable income of the 8156 italian households supplied by Bank of Italy (2016) where the households are partitioned in four subpopulations according to the number of family members and the total income is the sum of four sources.
An interactive MATLAB TOOL for the estimation of the directional mobility index compared to some other mobility measures
by Danilo Aringhieri, Camilla Ferretti pages: 36 Download
The Directional Mobility Measurement tool (DMMtool.m) is a free interactive instrument, which computes in MATLAB the directional mobility index by automatically importing and elaborating the data from a wide-format panel, not necessarily balanced, stored in a ‘.xls’ or ‘.xlsx’ spreadsheet, for a single, quantitative, ‘size-type’ scalar variable. We will imagine a sample of firms observed over many successive years to assess their tendency to upsize or downsize. The researcher is requested to provide all the inputs necessary for calculations: the name of the data file, the extremes of intervals partitioning the variable’s domain, the starting and the final time of the transition, the shape of all parameters within the directional index. The statistical units are automatically allocated into the states, the first-order not-stationary Markov transition matrix is estimated between the selected temporal extremes, the directional index is evaluated. Its value is compared with some other not-directional mobility indices based on the transition matrix between the same selected dates. Accuracy of each input respect to its constraints is verified.

Download Supplementary Material.