A combined method based on kurtosis indexes for estimating p in non-linear Lp-norm regression

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Optimal forecasting accuracy using Lp‑norm combination

A well-known result in statistics is that a linear combination of two-point forecasts has a smaller Mean Square Error (MSE) than the two competing forecasts themselves (Bates and Granger in J Oper Res Soc 20(4):451–468, 1969). The only case in which no improvements are possible is when one of the single forecasts is already the optimal one in terms of MSE. The kinds of combination methods are various, ranging from the simple average (SA) to more robust methods such as the one based on median or Trimmed Average (TA) or Least Absolute Deviations or optimization techniques (Stock and Watson in J Forecast 23(6):405–430, 2004). Standard regression-based combination approaches may fail to get a realistic result if the forecasts show high collinearity in several situations or the data distribution is not Gaussian. Therefore, we propose a forecast combination method based on Lp-norm estimators. These estimators are based on the Generalized Error Distribution, which is a generalization of the Gaussian distribution, and they can be used to solve the cases of multicollinearity and non-Gaussianity. In order to demonstrate the potential of Lpnorms, we conducted a simulated and an empirical study, comparing its performance with other standard-regression combination approaches. We carried out the simulation study with diferent values of the autoregressive parameter, by alternating heteroskedasticity and homoskedasticity. On the other hand, the real data application is based on the daily Bitfnex historical series of bitcoins (2014–2020) and the 25 historical series relating to companies included in the Dow Jonson, were subsequently considered. We showed that, by combining diferent GARCH and the ARIMA models, assuming both Gaussian and non-Gaussian distributions, the Lp-norm scheme improves the forecasting accuracy with respect to other regression-based combination procedures

Evaluation Indices of the Judicial System and ICT Developments in Civil Procedure

The reform that by Presidential Decree (13th February 2001, n. 123) introduced “telematics civil procedure” in Italian judicial system has the objective of a more efficient and rapid justice. The combination of justice and Information and Communications Technology is now a path to be not only in terms of functionality and cost management, but also to adjust the Italian quality standards respect to the other European states: it is shown telematics civil procedure in its architecture, in its draft implementation and benefits arising from its actual use. From the statistical point of view as an indirect measure of the judiciary evaluation, we analyze the indices of average length in the various proceedings, as the main tool for assessing the efficiency court, noting the repeated condemnations against the Italian state, by the European Court of Human Rights, for failure to comply with the principle of reasonable duration of the process, a further confirmation of the unfortunate situation that our country is facing on the topic of judicial authority

IL RUOLO DEI BIG DATA NELLE STRATEGIE DI APPRENDIMENTO

Il lavoro richiama i concetti fondamentali sui Big Data, evidenziando, in particolare, le applicazioni delle tecniche di analisi e delle tecnologie a supporto nell’ambito dell’elearning. Gli enormi volumi di dati disponibili al giorno d’oggi stanno diventando protagonisti dell’e-learning, ad esempio per identificare in modo proattivo le esigenze di apprendimento degli allievi. Lo studio confronta i Big Data con il Data Mining, le tecniche di e-learning e di learninganalytics. Sono accennati i principi di etica e di privacy da osservare e sono, infine, riportati i principali vantaggi derivanti dall’adozione dei Big Data nel settore educativo

Chapter Exploring competitiveness and wellbeing in Italy by spatial principal component analysis

Well being is a multidimensional phenomenon, that cannot be measured by a single descriptive indicator and that, it should be represented by multiple dimensions. It requires, to be measured by combination of different dimensions that can be considered together as components of the phenomenon. This combination can be obtained by applying methodologies knows as Composite Indicators (CIs). CIs are largely used to have a comprehensive view on a phenomenon that cannot be captured by a single indicator. Principal Component Analysis (PCA) is one of the most popular multivariate statistical technique used for reducing data with many dimension, and often well being indicators are obtained using PCA. PCA is implicitly based on a reflective measurement model that it non suitable for all types of indicators. Mazziotta and Pareto (2013) in their paper discuss the use and misuse of PCA for measuring well-being. The classical PCA is not suitable for data collected on the territory because it does not take into account the spatial autocorrelation present in the data. The aim of this paper is to propose the use of Spatial Principal Component Analysis for measuring well being in the Italian Provinces

Comparative Analysis of Student Learning: Technical, Methodological and Result Assessing of PISA-OECD and INVALSI-Italian Systems .

PISA is the most extensive international survey promoted by the OECD in the field of education, which measures the skills of fifteen-year-old students from more than 80 participating countries every three years. INVALSI are written tests carried out every year by all Italian students in some key moments of the school cycle, to evaluate the levels of some fundamental skills in Italian, Mathematics and English. Our comparison is made up to 2018, the last year of the PISA-OECD survey, even if INVALSI was carried out for the last edition in 2022. Our analysis focuses attention on the common part of the reference populations, which are the 15-year-old students of the 2nd class of secondary schools of II degree, where both sources give a similar picture of the students

Distribution-based entropy weighting clustering of skewed and heavy tailed time series

The goal of clustering is to identify common structures in a data set by forming groups of homogeneous objects. The observed characteristics of many economic time series motivated the development of classes of distributions that can accommodate properties, such as heavy tails and skewness. Thanks to its flexibility, the skewed exponential power distribution (also called skewed generalized error distribution) ensures a unified and general framework for clustering possibly skewed and heavy tailed time series. This paper develops a clustering procedure of model-based type, assuming that the time series are generated by the same underlying probability distribution but with different parameters. Moreover, we propose to optimally combine the estimated parameters to form the clusters with an entropy weighing k-means approach. The usefulness of the proposal is shown by means of application to financial time series, demonstrating also how the obtained clusters can be used to form portfolio of stocks.Peer ReviewedPostprint (published version

Psychological stress in nurses assisting Amyotrophic Lateral Sclerosis patients: a statistical analysis based on Non-Parametric Combination test

We aimed to evaluate the psychological, emotional and relational burden of nurses who provide assistance to patients affected by Amyotrophic Lateral Sclerosis (ALS). A survey was conducted by administering a questionnaire, the “Health Professions Stress and Coping Scale”, which proposes some potentially stressful work situations. The questionnaire was administered to 105 nurses working in hospitals where there is a ward for patients suffering from ALS. We used the "Non-Parametric Combination Test", a multivariate methodology based on permutation solution, widely applicable in various research contexts. Firstly, we investigated the areas of stress; then, the attention was focused on the different coping strategies adopted by respondent nurses within each stress area. The analyses were stratified according to different confounding factors, in order to control their potential effect. The results show the presence of an average level of stress, regardless of gender and educational status. Furthermore, there are significant differences in stress levels in subjects classified according to the ward in which they operate and a positive correlation between higher stress levels and the number of service years was found. In the future this study could also be of interest to nurses working in wards with potentially stressful situations