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Helping the interpretation of coefficient estimates of multinomial logit models where the response variable takes three possible values (trinomial) through ternary plots.

The purpose of this paper is to propose an estimation strategy that accounts for both problems by performing a two-step calibration.

Graphical and analytical tools for analysis and goodness-of-fit tests for the Pareto and the Log-normal distributions.

This contribution aims at developing a strategy to reduce the bias and produce more reliable inference for spatial models with location errors. The proposed estimation strategy models both the spatial stochastic process and the coarsening mechanism by means of a marked point process.

We develop a likelihood-ratio test for discriminating between the g-and-h and the g distribution, which is a special case of the former obtained when the parameter h is equal to zero.

A characterizing property of Zenga (1984) inequality curve is exploited in order to develop an estimator for the extreme value index of a distribution with regularly varying tail.

This paper proposes a mixed sampling strategy for finite populations where a portion of the units is not correctly geo-referenced.

An endemic-epidemic time-series mixed-effects generalized linear model for areal disease counts has been implemented to understand and predict spatio-temporal diffusion of the phenomenon.

This paper aims at investigating empirically whether and to what extent the containment measures adopted in Italy had an impact in reducing the diffusion of the COVID-19 disease across provinces.

The paper proposes an empirical solution that helps to mitigate spatial dependence in regression residuals when no information is available about unit positions.