Statistica & Applicazioni Open Access Journal


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: 1824-6672
ISSN digitale: 2283-6659

In this issue


Assessing dimensions of the city’s reputation
by Roberto Fasanelli, Ida Galli, Alfonso Piscitelli pages: 25 Download
In social psychology, reputation has been studied with reference to different objects (individuals, brands, cities, etc.) and methodologically, measured discerning between its subdimensions. In this article, city reputation is  operationally defined, by using the validated City Reputation Indicators scale. This empirical tool is useful to evaluate the separate dimensions of city reputation independently. Data, obtained from a survey administered in the city of  Naples, were analysed using the Classification-tree, a non-parametric procedure, widely used in supervised classification. We also used the Spearman rank correlation, in order to assess the degree of association between overall  citizen satisfaction and overall city reputation. The classification tree has made possible the identification of the key path which better identifies people considering Naples a city with a good reputation. Furthermore, results also show the main constituents of city reputation.
Analysis of structural break in VAR (k) time series model: a bayesian approach
by Umme Afifa, Varun Agiwal, Jitendra Kumar pages: 20 Download
Vector autoregressive (VAR) model is the most popular modeling tool in macroeconomics. This study considers a Bayesian framework for VAR(k) model with a structural break in the mean. The structural change problem in VAR is of  theoretical and practical importance in reference to the economic time series data. The main motivation of the study is to identify the impact of the break in the series and estimate the model parameters in the presence of the break  considering appropriate prior assumptions. A simulation study and empirical analysis of the net asset value of national pension schemes for different fund managers have been carried out to justify the proposed mechanism.
The evaluation of credit risk using survival models: an application on Italian SMEs
by Andrea Marletta pages: 18 Download
The financial literature proposed many contributions to measure the credit risk, in this work a survival approach is proposed to reach this purpose. Having available the survival times for each credit line, the choice was oriented to  survival models to evaluate the pathological death of the loan. A survival analysis was conducted on a dataset containing 5322 credits for Italian companies through a Cox model considering some risk factors about both the company  and the loan. The selected Cox model led to the identification of risk profiles representing different situations in terms of probability of insolvency.
A statistical assessment on abrupt change and trend analysis of rice production
by Christophe Chesneau, Polisetty Kalpana, Paidipati Kiran Kumar pages: 11 Download
The most common method for studying historical data is to use regression methods and predictive modeling on time series data. The parametric methodology for time series data analysis is a customary method when the data are  available on a continuous scale. However, most of the time, the data availability may be on a categorical or ordinal scale. Hence, the nonparametric methodology is more rational in handling time series data. This study considers two  prominent non-parametric methods, namely Pettitt’s test and Buishand’s range test. In particular, we examine an abrupt change in the annual data of rice production during the period 1980-2020 by these methods. The study  continued to assess the performance of rice production with the presence and absence of trend as performed by the Mann-Kendall test and the trend measured by Sen’s slope estimator. According to the findings, the second time period’s average growth rate has improved slightly but not as significantly as the first time period’s.
Annual content
pages: 2 Download

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