17 research outputs found
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
Analisi di politica monetaria della Spagna: il principio di Taylor applicato alla condotta del banco de Espana e della BCE.
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
Gene expression profiling in response to UV-B radiation in different Populus alba clones
Off-stoichiometry, Vacancy Trapping and Pseudo-irreversible First-cycle Capacity in LiNiO2
We demonstrate that the ubiquitous off-stoichiometry of LiNiO2 in the form of Li_{1-z}Ni_{1+z}O2 slows the kinetics of the material both by diminishing the number of charge carriers and increasing the length of diffusion paths. Excess Ni in the Li layer, Ni_{Li}, exerts an attractive potential on Li vacancies, lowering their energy with respect to defect-free regions. This attractive field extends over a radius of two lattice sites and also considerably lowers the barrier for a Li vacancy to approach the defect, effectively making Ni_{Li} a sink for lithium vacancies. A similar argument can be made for divacancies, which are split by Ni_{Li} and pinned in the form of single vacancies. In addition to pinning effects, which could vary depending on the state of charge, Ni_{Li} also constitutes an obstacle to Li migration, because it is rather immobile and does not undergo site-exchange with an adjacent Li vacancy