The Compass for Statistical Researchers

We have hiked many miles alongside several professors as we traversed our statistical path -- a regime switching trail which changed direction following a class on the foundations of our discipline. As we play the game of research in that limbo between student and academic, one thing among Prof. Bernardi's teachings has never been more clear: to draw a route in the research map you not only need to know your destination, but you must also understand where you are and how you arrived there

Bayesian model averaging over tree-based dependence structures for multivariate extremes

Describing the complex dependence structure of extreme phenomena is particularly challenging. To tackle this issue we develop a novel statistical algorithm that describes extremal dependence taking advantage of the inherent hierarchical dependence structure of the max-stable nested logistic distribution and that identifies possible clusters of extreme variables using reversible jump Markov chain Monte Carlo techniques. Parsimonious representations are achieved when clusters of extreme variables are found to be completely independent. Moreover, we significantly decrease the computational complexity of full likelihood inference by deriving a recursive formula for the nested logistic model likelihood. The algorithm performance is verified through extensive simulation experiments which also compare different likelihood procedures. The new methodology is used to investigate the dependence relationships between extreme concentration of multiple pollutants in California and how these pollutants are related to extreme weather conditions. Overall, we show that our approach allows for the representation of complex extremal dependence structures and has valid applications in multivariate data analysis, such as air pollution monitoring, where it can guide policymaking

Multilingualism in South Tyrol: between old fears and new challenges

This contribution illustrates the specific sociolinguistic landscape of South Tyrol, an Italian province where the majority of the population is German-speaking. First, we explain to what extent the division of society into separate language groups has remained in force to this day and to which degree this hinders the achievement of German-Italian bilingual everyday life. Secondly, the discussion focusses on residents with foreign citizenship almost 10 % of the population) and how they deal with and live this local sociolinguistic situation. Against the backdrop of the changed transformed socio-demographic situation, we discuss possible strategies as to how the longstanding focus on bilingualism may be expanded to include multilingualism, which is already mostly in place

Multilingualism in South Tyrol: between old fears and new challenges

This contribution illustrates the specific sociolinguistic landscape of South Tyrol, an Italian province where the majority of the population is German-speaking. First, we explain to what extent the division of society into separate language groups has remained in force to this day and to which degree this hinders the achievement of German-Italian bilingual everyday life. Secondly, the discussion focusses on residents with foreign citizenship almost 10 % of the population) and how they deal with and live this local sociolinguistic situation. Against the backdrop of the changed transformed socio-demographic situation, we discuss possible strategies as to how the longstanding focus on bilingualism may be expanded to include multilingualism, which is already mostly in place

Bayesian Clustering and Dimension Reduction in Multivariate Extremes

Describing the complex dependence structure of multivariate extremes is particularly challenging and requires very versatile, yet interpretable, models. To tackle this issue we explore two related approaches: clustering and dimension reduction. In particular, we develop a novel statistical algorithm that takes advantage of the inherent hierarchical dependence structure of the maxstable nested logistic distribution and that uses reversible jump Markov chain Monte Carlo techniques to identify homogeneous clusters of variables. Dimension reduction is achieved when clusters are found to be completely independent. We signifficantly decrease the computational complexity of full likelihood inference by deriving a recursive formula for the nested logistic model likelihood. The algorithm performance is verified through extensive simulation experiments which also consider different likelihood procedures. The new methodology is used to investigate the dependence relationships between extreme concentration of multiple pollutants across a number of sites in California and how these pollutants are related to extreme weather conditions. Overall, we show that our approach allows for the identification of homogeneous clusters of extremes and has valid applications in multivariate data analysis, such as air pollution monitoring, where it can guide policymaking