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
Relative-importance assessment of explanatory variables in generalized linear models: an entropy-based approach
The object of the present paper is to propose a method for relative-importance assessment of explanatory variables in generalized linear models, through an analysis of the variation of entropy of the response variable. First, the problem is reviewed in the ordinary regression model and some criteria to be met by a suitable measure are emphasized. Second, the logic of variation in entropy is introduced, for the assessment both of the predictive power of the whole model and of the relative importance of each variable. Third, the occurrence of a causal order of variables is discussed and a new approach is proposed to deal with cases where this order lacks. Finally, the ability to meet the listed criteria is checked for the proposed measure and two relevant examples (logit model and twoway ANOVA model) are provided, both with numerical applications.
In this paper the features of some distribution models used in the literature to depict income distributions are analysed. The analysis is based on the inequality curves generated by such models. In particular, the role of the parameters related to the inequality curves is investigated, also by considering the influence of their variations from a pointwise perspective.
A random effect model for the evolution of International Cricket Test matches evidenced from 1870 to 2016
This article has proposed a random effects model to understand the trend of International Test Cricket results. The data is extracted for all the test matches played between 1870 and 2016 for countries that play test matches for at least four decades. The random effects model is applied for to study the countrywise and decadewise performance of a country at home and away matches. Analysis shows a very clear bias in the outcome towards home country winning across decades as well as for individual countries. The trend is discussed with implications of the evolution of the game itself so as to retain the tradition of cricket and to enhance the overall performance of a player.
In this paper, several insights on the Zenga’s approach for the measurement of Kurtosis are provided. These insights mainly regard the connections between Kurtosis and Concentration indexes and the relation between the Kurtosis diagram and an extension of the well-known Lorenz curve, i.e. the relative first incomplete moment function. Special attention is also given to the relations of the Kurtosis partial stochastic ordering with the Lorenz and convex partial stochastic orderings. The obtained results are applied in order to study the Kurtosis ordering in the Generalized Lognormal Distribution.
This study considers an empirical investigation of the hourly PUN, which is the spot price of a megawatt on the Italian electricity market. This price is characterised by strong intra-day seasonality, i.e., hourly effects, which influence the level and volatility price, and many parameters are involved in the ARMA-GARCH modelling process. In order to reduce the number of parameters, an alternative modelling approach is presented based on structural time series modelling, i.e., an autoregressive model with seasonal effects at the PUN level and a stochastic volatility model (with seasonal effects) for the PUN volatility. This modelling approach allows us to treat seasonality as a latent stochastic component, which is governed by only a few parameters. The results obtained using this method demonstrate that the proposed modelling approach has beneficial strengths, and thus it should be developed further.