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: Scopus - Google Scholar - Current Index to Statistics - Ulrich's Periodicals Directory - SCImago Journal & Country Rank
- Available on: Torrossa - EBSCO Discovery Service
In this issue
In this paper, we estimate aversion to rank inequality (ATRI) underlying selected Italian income inequality indices, I, notably the Pietra index, the Bonferroni index and the “new” Zenga index. We measure ATRI by the parameter v of the generalised Gini index G(v). ATRI is distinct from aversion to income inequality, as measured by parameter ε of Atkinson’s index A(ε). We propose eliciting v from the equation I = GE(v). As, in general, an analytical solution to this equality can be cumbersome, we retrieve v from the empirical equation Iˆ = Gˆ (v) where the symbols Iˆ and Gˆ (v) denote the estimates of I and G(v), respectively. We also calculate the benchmark income x* such that adding a small income to it does not affect inequality. In this paper, we solve the equation using the estimates of the Italian inequality indices for Poland from 2000 to 2017. We have found, on average, v≈1.5 for the Pietra index, v ≈ 3 for the Bonferroni index, and v ≈ 11 for the Zenga index.
Application of Nonparametric Stability Methods in Chickpea (Cicer Arietinum L.) Crop Under Diverse Environments
by Khadar Babu SK, Ganga Ganga Rao NVPR, S. Parthasarathy, Mamta Sharma, Anilkumar Vemula pages: 14 Download
Apply different nonparametric tests for genotype x environment interactions (GEI) on 27 chickpea genotypes evaluated for Fusarium wilt across 10 environments. Results of nonparametric tests of Bredenkamp and Van der de kroon and parametric test of combined analysis of variance across environments indicated the presence of both crossover and non-crossover interactions of GEI. The results of the Principal Component Analysis and rank correlation of nonparametric stability statistics would be indicated to selection of static and dynamic stability genotypes which had low wilt. Rank Sum Method (RSM) and mean wilt had significant positive correlation and indicated that RSM was the best parameter to identify low wilt and high stability genotypes. Among the non-parametric stability, RSM would be useful for simultaneous selection for low wilt and stability and G6, G11, G14, G15, G16, G23, G25, G26 and G27 as of dynamic stability and wide adaptation, G11, G25 and G26 had lowest wilt and high stability and which becomes statistic stability. The objectives of this investigation were (i) Apply nonparametric tests to investigate the crossover and non-crossover GE interaction in multi-environment trials, (ii) Study the relationships among different nonparametric stability statistics on selection of stable chickpea genotypes.
We describe a model-free, fully data-driven approach to simulating random draws from a continuous multivariate distribution. The proposed technique is an extension of the smoothed bootstrap which explicitly accounts for local differences in the dispersion of individual data points in the sample. Results from a number of simulation experiments suggest that in many cases, the procedure presented strikes a favourable balance between the conflicting objectives of adequately reflecting key characteristics of the underlying distributions and smoothing out the gaps between the individual data points in the sample. An exemplary application indicates that the proposed procedure can be used for model selection purposes by comparing competing specifications of a given regression model with respect to their out-of-sample prediction quality.
JUNIOR RESEARCHERS’ SECTION
Exploring financial microblogs: analysis of users’ trading profiles with multivariate statistical methods
StockTwits is a Social Media focused on finance that is receiving increasing attention from finance experts and enthusiasts. In this work, StockTwits’ users are studied considering some of their self-declared characteristics, such as trading experience, holding period of the stocks, and trading approach. A Correspondence Analysis is carried out to investigate the relationships among these characteristics, the Simple Correspondence Analysis is applied to study the relationships between the approach and the holding period. The association between these variables and the experience is studied with the Multiple Correspondence Analysis. In the end, a cluster analysis carried out with
hierarchical clustering is used to study the structure of the StockTwits community on the basis of the same characteristics. The analyses highlighted that the way users label their own approach and primary holding period reflect the objective relation linking technical strategy with short-term investments and fundamental approach with long-term ones. Moreover, it showed a weak relation of the experience in trading with the other features, configuring self-reported experience as a more cross-sectional characteristic.
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