Papers by Riccardo Massari
In recent years, increasing attention has been paid to household impoverishment in Italy. Most of... more In recent years, increasing attention has been paid to household impoverishment in Italy. Most of works dealing with this issue are based on summary statistics, which do not capture the whole information contained in the income distribution. The paper applies a non-parametric tool, the "relative distribution", to Italian household income data during 2002-2004. The relative density function is a proper density function which compares two distributions observed in different years, in order to describe patterns of differences on the entire income scale. This approach also allows for a decomposition of the relative distribution density function to isolate changes due to differences in location from changes due to differences in shape, thus providing further insights into the analysis of income polarization. During the 2000's there was a significant location effect, but also an increased income polarization, which has particularly affected incomes below the median. Social group analyses, according to the main income source of the household, show significant re-distribution effects within groups.
This paper examines how spatial price differentials affect income distribution in Italy. The dist... more This paper examines how spatial price differentials affect income distribution in Italy. The distribution of household income is “reshuffled” after controlling for the purchasing power of households residents in different regions, but only when housing price variations are included in the PPP index. Poor households living in Southern Italy alleviate their relative condition, but concentration of poverty still holds in
Our paper empirically evaluates the magnitude of the disparities across European countries and re... more Our paper empirically evaluates the magnitude of the disparities across European countries and regions in the demand for redistribution in the 2000's. We identify which are the individual characteristics and the contextual variables, at country and also at regional level, that contribute the most in predicting the observed different support for redistribution. Demand for redistribution is modelled in a multilevel
The European Union Survey on Income and Living Conditions (EU-SILC) is the main source of informa... more The European Union Survey on Income and Living Conditions (EU-SILC) is the main source of information about living standards and poverty in the member states of the European Union. It provides reliable statistics at national level but sample sizes do not allow reliable estimates at sub-national level, despite a rising demand from policy makers and local authorities. We provide a comprehensive map of median income, inequality (Gini coefficient and Lorenz curve) and poverty (poverty rates), at country and regional levels, based on the equivalized household income in all the countries in which EU-SILC is conducted. We focus on personal income distribution within regions as opposed to per capita income distribution across regions to give a deeper insight into regional disparities. Small-area estimation is applied to improve estimates in regions with small sample size. Uncertainty of such complex non-linear statistics is assessed by bootstrap methods. Household-level sampling weights are taken into account in both the estimates and their relative bootstrapped standard errors.
ABSTRACT Self-Organizing Maps (SOMs) consist of a set of neurons arranged in such a way that ther... more ABSTRACT Self-Organizing Maps (SOMs) consist of a set of neurons arranged in such a way that there are neighbourhood relationships among neurons. Following an unsupervised learning procedure, the input space is divided into regions with common nearest neuron (vector quantization), allowing clustering of the input vectors. In this paper, we propose an extension of the SOMs for data imprecisely observed (Self-Organizing Maps for imprecise data, SOMs-ID). The learning algorithm is based on two distances for imprecise data. In order to illustrate the main features and to compare the performances of the proposed method, we provide a simulation study and different substantive applications.
ABSTRACT In this paper, following a fuzzy approach and adopting an autoregressive parameterizatio... more ABSTRACT In this paper, following a fuzzy approach and adopting an autoregressive parameterization, we propose a robust clustering model for classifying time series. In particular, by adopting a fuzzy partitioning around medoids approach, the suggested clustering model is able to define the so-called medoid time series, which is a representative time series of each cluster, and the membership degrees of each time series to the different clusters. The robustness of the proposed clustering model is guaranteed by the adoption of a suitable robust metric for time series, i.e. the so-called exponential distance measure. In this way, the clustering model is able to tolerate the presence of outlier time series in the clustering process. In particular, it is capable of neutralizing and smoothing the disruptive effect of outlier time series, preserving the original clustering structure of the dataset, by assigning to outlier time series approximately the same membership degrees across clusters. To illustrate the usefulness and effectiveness of the suggested time series clustering model, a simulation study and an application to air pollution time series are carried out. Comparison with some existing clustering procedures suggested in the literature shows several advantages of the proposed model.
ABSTRACT Following a nonparametric approach, we suggest a time-series clustering method. Our clus... more ABSTRACT Following a nonparametric approach, we suggest a time-series clustering method. Our clustering approach combines the benefits connected to the interpretative power of the nonparametric representation of the time series, and the clustering and vector quantization informational gain produced by the adopted unsupervised neural networks technique, enhanced with the self-organizing maps ordering and topological preservation abilities. The proposed clustering method takes into account a composite wavelet-based information of the multivariate time series by adding to the information connected to the wavelet variance, namely the influence of variability of individual univariate components of the multivariate time series across scales, the information associated to wavelet correlation, represented by the interaction between pairs of univariate components of the multivariate time series at each scale, and then suitably tuning the combination of these pieces of information. In order to assess the effectiveness of the proposed clustering approach, a simulation study and an empirical application are shown. Copyright © 2013 John Wiley & Sons, Ltd.
Segmentation has several strategic and tactical implications in marketing products and services. ... more Segmentation has several strategic and tactical implications in marketing products and services. Despite hard clustering methods having several weaknesses, they remain widely applied in marketing studies. Alternative segmentation methods such as fuzzy methods are rarely used to understand consumer behaviour. In this study, we propose a strategy of analysis, by combining the Bagged Clustering (BC) method and the fuzzy C-means clustering method for fuzzy data (FCM-FD), i.e., the Bagged fuzzy C-means clustering method for fuzzy data (BFCM-FD). The method inherits the advantages of stability and reproducibility from BC and the flexibility from FCM-FD. The method is applied on a sample of 328 Chinese consumers revealing the existence of four segments (Admirers, Enthusiasts, Moderates, and Apathetics) of the perceived images of Western Europe as a tourist destination. The results highlight the heterogeneity in Chinese consumers’ place preferences and implications for place marketing are offered.
Information Sciences, 2010
... The fuzzy regression deviation: [ ] ( ) 222 ~ ~ ~ ~ ~ jjj jjjjjjjjjjj umlmmSSR UUU 11um11lm1m... more ... The fuzzy regression deviation: [ ] ( ) 222 ~ ~ ~ ~ ~ jjj jjjjjjjjjjj umlmmSSR UUU 11um11lm1m +-++- 4.2) The fuzzy error or residual deviation: [ ] ( ) 222 jjj jjjjjjSSEUUU umumlmlmmm ***** 4.3) The deviation (4.2) is the variability of Y ...
Information Sciences, 2011
... Results in Table 3 are also confirmed by Figure 15, where are reported the observed data and ... more ... Results in Table 3 are also confirmed by Figure 15, where are reported the observed data and the interpolated lines for the three models, and also for the LS model proposed by Coppi et al. (2006). P1 estimates are heavily upward biased. ...
Journal of Applied Statistics, 2012
The European Union Statistics on Income and Living Conditions (EU-SILC) is the main source of inf... more The European Union Statistics on Income and Living Conditions (EU-SILC) is the main source of information about poverty and economic inequality in the member states of the European Union. The sample sizes of its annual national surveys are sufficient for reliable estimation at the national level but not for inferences at the sub-national level, failing to respond to a rising
Oxford Bulletin of Economics and Statistics, 2013
ABSTRACT We evaluate the magnitude of the disparities in the demand for redistribution across Eur... more ABSTRACT We evaluate the magnitude of the disparities in the demand for redistribution across European countries and American states during the 2000s. Modelling the demand for redistribution in a multilevel framework, we identify the determinants that contribute the most in predicting support for redistribution. We observe that individual characteristics and contextual variables are associated with demand for redistribution in the same way in Europe and in the US, whereas others exert different influences on the probability of supporting redistribution. We find important differences from some well‐established evidence obtained from data collected for the 1980s and the 1990s.
Fuzzy Sets and Systems, 2013
ABSTRACT In the context of human activity pattern analysis, we adopt a fuzzy clustering around me... more ABSTRACT In the context of human activity pattern analysis, we adopt a fuzzy clustering around medoids approach to classify ordered sequences (paths). These sequences represent patterns of individual behavior in an actual or virtual space–time domain. A fuzzy approach is suitable for path data, since sequences of human activities are typically characterized by switching behaviors, which are likely to produce overlapping clusters. We adopt a partitioning around medoids strategy since in human activity patterns analysis it is useful to represent each cluster by means of an observed (not fictitious) prototype (medoid). To measure pairwise distances among all sequence pairs we make use of the Levenshtein distance, which allows for the comparison between sequences of different length and explicitly takes into account the sequential nature of the data. We also consider two robust versions of the fuzzy clustering algorithm based, respectively, on the noise cluster and on the trimming technique. Robust algorithms deal with noisy observations, which are likely to occur in this framework and could provide an improvement to the standard model. We show several applications on sequence data, regarding different research areas, like Web usage mining, travel behavior, tourists and shopping paths.
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Papers by Riccardo Massari