The paper proposes a new nonparametric prior for two-dimensional vectors of survival functions (S1, S2). The definition we introduce is based on the notion of L´evy copula and it will be used to model, in a nonparametric Bayesian framework, two-sample survival data. Such an application will...

The paper proposes a new nonparametric prior for two-dimensional vectors of survival functions (S1,S2). The definition we introduce is based on the notion of Lévy copula and it will be used to model, in a nonparametric Bayesian framework, two-sample survival data. Such an application will yield...

The paper proposes a new nonparametric prior for two–dimensional vectors of survival functions (S1, S2). The definition we introduce is based on the notion of L´evy copula and it will be used to model, in a nonparametric Bayesian framework, two–sample survival data. Such an application will...

This paper aims at providing a Bayesian parametric framework to tackle the accessibility problem across space in urban theory. Adopting continuous variables in a probabilistic setting we are able to associate with the distribution density to the Kendall's tau index and replicate the general...

This paper develops a novel approach studying the distribution of the regional population density across space. We work in a Bayesian parametric framework. Exploiting the Gamma distribution, we are able to introduce heterogeneity across space without incurring any a priori definition of...

This study aims at assessing the evolution of the importance of spatial distance in citizens' location choices across time. We focus on the case of population distribution in Massachusetts. We handle a database including data for the years 1880-1890 and 1930-2010. By adopting a Bayesian...

This study proposes to investigate the effectiveness of modeling local spatial dependence through a conditionally autoregressive process (CAR) to picture the population distribution across space. Following the current literature, the idea is to model individual location preferences by focusing...

This study aims to investigate the extent to which history matters in shaping population distribution across space. In the wake of the current literature, the idea is to model individual location preferences by focusing on selected local determinants (neighborhood, education, income, amenities...

The definition of vectors of dependent random probability measures is a topic of interest in applications to Bayesian statistics. They, indeed, represent dependent nonparametric prior distributions that are useful for modelling observables for which specific covariate values are known. In this...

In Bayesian nonparametric inference, random discrete probability measures are commonly used as priors within hierarchical mixture models for density estimation and for inference on the clustering of the data. Recently it has been shown that they can also be exploited in species sampling...