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Compositional data consist of known compositions vectors whose components are positive and defined in the interval (0,1) representing proportions or fractions of a whole. The sum of these components must be equal to one. Compositional data is present in different knowledge areas, as in geology, economy, medicine among many others. In this paper, we introduce a Bayesian analysis for compositional regression applying additive log-ratio (ALR) transformation and assuming uncorrelated and correlated errors. The Bayesian inference procedure based on Markov Chain Monte Carlo Methods (MCMC). The methodology is illustrated on an artificial and a real data set of volleyball.
This paper introduces the R package slm which stands for Stationary Linear Models. The package contains a set of statistical procedures for linear regression in the general context where the error process is strictly stationary with short memory. We
Most research on regression discontinuity designs (RDDs) has focused on univariate cases, where only those units with a forcing variable on one side of a threshold value receive a treatment. Geographical regression discontinuity designs (GeoRDDs) ext
We introduce a computational scheme for calculating the electronic structure of random alloys that includes electronic correlations within the framework of the combined density functional and dynamical mean-field theory. By making use of the particul
Bayesian Additive Regression Trees(BART) is a Bayesian nonparametric approach which has been shown to be competitive with the best modern predictive methods such as random forest and Gradient Boosting Decision Tree.The sum of trees structure combined
Insurance industry is one of the most vulnerable sectors to climate change. Assessment of future number of claims and incurred losses is critical for disaster preparedness and risk management. In this project, we study the effect of precipitation on