Search

Italian databases about tax disputes are available for several years. The Ministry of Economy and Finance analyzes these data quarterly, by using mostly univariate statistics. However, multidimensional and multivariate statistical analysis of tax proceedings becomes necessary to overcome the limits of univariate statistics. Here, we aim to study the association between the tax authorities and the geographic area of resolved tax disputes by applying the log-ratio analysis. The log-ratio analysis presents many advantages, among which, the computation and the visual representation of the odds ratios which represent a very simple measure of association.

Ordinal logistic regression is a powerful tool for modeling the dependence relationship between a set of regressors and an ordinal response variable. However, it necessitates careful consideration of multicollinearity among the regressors and examination for outliers. For dealing with these problems in ordinal logistic regression models, we develop an approach based on robust principal components. The new robust approach will be applied for assessing judges’ performances.

ANalysis Of VAriance (ANOVA) is a method to decompose the total variation of the observations into sum of variations due to different factors and the residual component. When the data are nominal, the usual approach of considering the total variation in response variable as measure of dispersion about the mean is not well defined. Light and Margolin (1971) proposed CATegorical ANalysis Of VAriance (CATANOVA), to analyze the categorical data. Onukogu (1985) extended the CATANOVA method to two-way classified nominal data. The components (sums of squares) are, however, not orthogonal. Singh (1996) developed a CATANOVA procedure that gives orthogonal sums of squares and defined test statistics and their asymptotic null distributions. In order to study which exploratory categories are influential factors for the response variable we propose to apply Non-Symmetrical Correspondence Analysis (D'Ambra and Lauro, 1989) on significant components. Finally, we illustrate the analysis numerically, with a practical example.

In this work it has been carried out a regression analysis of the experimental data of displacement catamarans tested at the towing tank of the Dipartimento di Ingegneria Navale di Napoli (DIN). The recorded data have been obtained testing 3 different hull forms in the speed range corresponding to Froude number up to 0.7, combining 6 clearances and 3 displacements, for a total of 42 catamaran configurations. The regression model has been carried out studying the relationship, for assigned Fn values, between a set of dependent variables (CR×103) and a set of independent variables (L/▽1/3, LCB/LCF, b/T, C B, IE, AT/AM, S/L). The data show independent variables strongly correlated, missing values of the residuary resistance at higher velocity for some catamaran configurations and a limited number of observations, so the Ordinary Least Squares (OLS) cannot be applied. The most proper statistical methodology to analyse the data with these characteristics is the Partial Least Squares (PLS) regression. The regression model has been also validated by the PRediction Error Sum of Squares (PRESS).

In this paper, we aim to study the key factors which affect the environmental quality of the sea. The Fp-ratio, utilized as an indicator of the trophic state of the coastal waters, is classified into three categories. Given the ordinal nature of the response variable, we believe ordinal logistic regression models are the most proper statistic methodology. As the regressors are strongly correlated, the parameters of ordinal logistic regression models cannot be estimated. For solving this problem and, in general, the consequences of multicollinearity, we develop an approach based on principal components. The new approach will be applied for estimating the Fp-ratio.

The paper investigates the response patterns of preferences towards sustainable tourism in Vallo di Diano area, Campania region, southern Italy, collected on a sample of local actors. Exploiting a model-based framework, results show that both contextual and subjective characteristics significantly affect the expressed preferences.

The aim of this study is to evaluate the Nitrogen Oxides (NOx) emissions trough an accurate analysis of vehicle driving behaviour. For this purpose, a three-way contingency table will be carried out, crossing the NOx emissions, the speed and the acceleration. This contingency table will be analysed by the partition of Marcotorchino index. To complement the survey Ordered Non-Symmetric Correspondence Analysis (ONSCA) will be applied.