Preface of the 'Symposium on Computational Statistical Methods'

摘要

In the last decades, computers power has increased exponentially and that power hasallowed the arisen of new and more complex computational statistical models and methods.In this symposium, we bring together researchers working or applying computational statistical. All the submitted papers in the symposium have been peer reviewed and selected on the basis of the originality, significance and clarity. In our symposium (by presenter order), Miguel Felgueiras investigate Gaussian mixtures with independent components and introduce a shifted and scaled t-Student distribution as an approximation for the distribution of Gaussian mixtures. Filipe J. Marques study the distribution of the linear combination ofindependent Gamma random variables. Ayana Mateus presents an R package implementation of several nonparametric randomness tests. Fernanda Figueiredo makes a comparison of sampling plans by variables using the bootstrap and Monte Carlo simulations. Luis M. Grilo apply the bootstrap methodology to construct control charts, before and after a Box-Cox transformation, and compare their robustness and performance.Joao P. Martins analyze the performance of a maximum likelihood computational algorithm for the prevalence rate estimation. Dora Prata Gomes explores the statistical software R for modeling spatial extreme precipitation data. Finally, Frederico Caeiro employ the Monte Carlo methodology to compare the finite sample behavior of two reduced bias estimators of the extreme value index.