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
On the decomposition by subpopulations of the point and synthetic Bonferroni inequality measures
This paper, by using the ‘‘two-step’’ approach proposed in Radaelli (2008, 2010) and in Zenga (2016) for the decomposition of the Zenga (2007) index, obtains the decomposition of the Bonferroni (1930) inequality measure. In the first step the Bonferroni point measure Vh(Y) is decomposed in a weighted mean of k x k relative differences between the mean Mg(Y) of subpopulation g and the lower mean Mhl(Y) of the subpopulation l; the weights are the product of their relative frequencies. From this decomposition, we obtain two decompositions of Vh(Y) into the within and the between components, and into the sum of the k contributions of each subpopulation. In the second step, the decompositions of the Bonferroni point index are extended, in a simple way, to the Bonferroni synthetic measure V(Y). We remark that the decomposition obtained in this paper is rather different from those proposed by Tarsitano (1990), and Barcena-Martin and Silber (2013). Actually, they provide only a decomposition by subpopulations of the Bonferroni synthetic index.
Using IRT models to quantify the strengths and difficulties questionnaire (SDQ) outcomes
Over the past decades an increasing number of studies have focused on the importance of behavioural problems of school children. Often, the assessment of children’s behavioural and emotional problems has been carried out using the Strengths and Difficulties Questionnaire (SDQ). This paper analyses the parents’ and teachers’ scores of SDQ for a sample of children aged 6 to 10 years who participated in a karate project in a public school. To handle the response options of the SDQ we relied on Item Response Theory (IRT) models. In particular, in this setting, we exploited the attractive features of the Linear Logistic Models with Relaxed Assumptions to measure the change in the SDQ dimensions parameter estimates which occurred over two measurement occasions: before and after the karate project. Notwithstanding the wide and ongoing use of the SDQ as brief behavioural screening there are no studies, to date, which have illustrated the usefulness of this class of IRT based model for assessing change in the SDQ dimensions parameter estimates over time. This paper aims to fill this gap and discusses the main results of the application.
Decomposition by subpopulations of the point and the synthetic Gini inequality indexes
Keywords: Gini Index, Point Inequality Index, Synthetic Inequality Index, Decomposition by Subpopulatios
Forecasting the steel product prices with the ARIMA model
The following paper describes the results of the forecast activity applied on steel prices time series, the method used in this analysis is a rolling window ARIMA regression. The forecast activity is built on prices time series that have been collected by anonymous weekly surveys sent to a sample of steel Italian operators. This pricing collection activity officially started in 2012 thanks to a tight cooperation between DMS stat Lab and Siderweb. After a short introduction, the statistical approach used for the forecast is deeply described and the time series of the four steel product prices considered in this analysis are introduced; then, the result of the forecasting exercise is presented together with final conclusion in the end of this paper.