The study analyzes the components of advertising spending for a group of European countries with stable total advertising spending over the period 1994-2007. Three components of advertising spending were considered: Electronic, Print, and Online. Our main objective was to study how the components were restructured within the period under study and to find clusters of similar countries. A specif...

the closed nature of geochemical data has been proven in many studies. compositional data have special properties that mean that standard statistical methods cannot be used to analyse them. these data imply a particular geometry called aitchison geometry in the simplex space. for analysis, the dataset must first be opened by the various transformations provided. one of the most popular of the a...

Compositional model checking refers to reducing the problem of model checking some property of the whole system to the problem of model checking its components. In this paper, we investigate compositional approach for model checking monadic least fixed point logic on transition systems assembled from the components by using the H-sum and the fusion operator. 1 Research Area – Main Themes The ma...

This work studies the influence of several compositional effects on thermal and reactive processes. First, impact using a fully model in context simulations is considered. Detailed phase behavior models rely descriptions oil up to tens components. Lumping large number components into smaller pseudo-components order reduce computational cost standard practice for simulations. schemes are typical...

Compositional approaches to verification offer a powerful means to address the challenge of scalability. In this paper, we develop techniques for compositional verification of probabilistic systems based on the assume-guarantee paradigm. We target systems that exhibit both nondeterministic and stochastic behaviour, modelled as probabilistic automata, and augment these models with costs or rewar...

This paper examines a dataset which is modeled well by the Poisson-Log Normal process and by this process mixed with Log Normal data, which are both turned into compositions. This generates compositional data that has zeros without any need for conditional models or assuming that there is missing or censored data that needs adjustment. It also enables us to model dependence on covariates and wi...

Outlier detection is an important task for the statistical analysis of multivariate data, because often the outliers contain important information about the data structure. In compositional data, represented usually as proportions subject to a unit sum constraint, the ratios between the parts (variables) contain the essential information. This inherent property is, however, incompatible with th...