Statistical Analysis of Greenhouse Gas Emission Trends under the Kyoto Protocol

Il rischio di un riscaldamento globale dovuto all’aumento delle emissioni di origine antropica dei gas serra è ormai accertato e la necessità di idonee contromisure è il tema ambientale più dibattuto a livello internazionale dell’ultimo decennio. Secondo gli obiettivi previsti dal Protocollo di Kyoto, le emissioni di gas serra dovranno ridursi complessivamente del 5%, entro il periodo 2008-2012, rispetto l’anno base. Nel presente lavoro si analizzano gli andamenti delle emissioni di gas serra, disaggregate nei principali settori, e di alcuni indicatori economici ed energetici relativi a diversi paesi dal 1990 al 2002. La dinamica degli andamenti e l’attuazione di politiche di riduzione, data la complessità dell’informazione nell’arco del periodo temporale considerato, sono studiate mediante un’analisi fattoriale dinamica

Patterns of change in α and β taxonomic and phylogenetic diversity in the secondary succession of semi-natural grasslands in the Northern Apennines

We studied the secondary succession in semi-natural grasslands (dry grasslands and hay meadows) located in the eastern side of the Tuscan Apennines (Tuscany, Central Italy). We compared these habitats, investigating: (i) the changes in species richness, composition and phylogenetic diversity during the succession; (ii) whether the trends in species loss and species turnover in taxonomic diversity matched those in phylogenetic diversity. We performed a stratified random sampling, in a full factorial design between habitat type and succession stage (60 sampled plots, 10 × 2 types of habitat × 3 stages of succession). We constructed a phylogenetic tree of the plant communities and compared the differences in taxonomic/phylogenetic α- and β-diversity between these two habitats and during their succession. We identified indicator species for each succession stage and habitat. Looking at α-diversity, both habitats displayed a decrease in species richness, with a random process of species selection in the earlier succession stages from the species regional pool. Nevertheless, in the latter stage of dry grasslands we recorded a shift towards phylogenetic overdispersion at the higher-level groups in the phylogenetic tree. In both habitats, while the richness decreased with succession stage, most species were replaced during the succession. However, the hay meadows were characterized by a higher rate of new species’ ingression whereas the dry grasslands became dominated with Juniperus communis. Accordingly, the two habitats showed similar features in phylogenetic β-diversity. The main component was true phylogenetic turnover, due to replacement of unique lineages along the succession. Nevertheless, in dry grasslands this trend is slightly higher than expected considering the major importance of difference in species richness of dry grasslands sites and this is due to the presence of a phylogenetically very distant species (J. communis)

A linear regression model for imprecise response

A linear regression model with imprecise response and p real explanatory variables is analyzed. The imprecision of the response variable is functionally described by means of certain kinds of fuzzy sets, the LR fuzzy sets. The LR fuzzy random variables are introduced to model usual random experiments when the characteristic observed on each result can be described with fuzzy numbers of a particular class, determined by 3 random values: the center, the left spread and the right spread. In fact, these constitute a natural generalization of the interval data. To deal with the estimation problem the space of the LR fuzzy numbers is proved to be isometric to a closed and convex cone of R3 with respect to a generalization of the most used metric for LR fuzzy numbers. The expression of the estimators in terms of moments is established, their limit distribution and asymptotic properties are analyzed and applied to the determination of confidence regions and hypothesis testing procedures. The results are illustrated by means of some case-studies. © 2010 Elsevier Inc. All rights reserved

Management of uncertainty in Statistical Reasoning: The case of Regression Analysis

AbstractStatistical Reasoning is affected by various sources of Uncertainty: randomness, imprecision, vagueness, partial ignorance, etc. Traditional statistical paradigms (such as Statistical Inference, Exploratory Data Analysis, Statistical Learning) are not capable to account for the complex action of Uncertainty in real life applications of Statistical Reasoning. A conceptual framework, called “Informational Paradigm”, is introduced in order to analyze the role of Information and Uncertainty in these complex contexts. Regression Analysis is taken as the reference problem for developing the discussion. Three basic sources of Uncertainty are considered in this respect: (1) uncertainty about the relationship between response and explanatory variables; (2) uncertainty about the relationship between the observed data and the “universe” of possible data; (3) uncertainty about the observed values of the variables (imprecision, vagueness). Some of the available methods for coping with these different types of Uncertainty are discussed in an orderly way, from the simpler cases where only one source at a time is dealt with, to the more complex ones where all sources act together. Probabilistic and Fuzzy-Possibilistic tools are exploited, in this connection. In spite of the recent relevant contributions in this domain, the weaknesses and deficiencies of the current procedures for managing Uncertainty in Regression Analysis, as well as in other areas of Statistics, are emphasized. The elements of a generalized system of Statistical Reasoning, capable to deal with the various sources of Uncertainty, are finally introduced and the lines for future investigation in this perspective are indicated