New Perspectives on Computational Statistics - Emerging Technologies and Applications: Emerging Technologies and Applications

Classical statistical methods were quite powerful for solving certain classes of problems. Still, as models required more complex structures and datasets grew, these methods no longer provided an accurate mathematical representation of reality. Meanwhile, advances in computing have enabled millions of simulations, allowing researchers to solve problems previously unrepresentable with classical statistical methods. Thus, a new branch of science, Computational Statistics, emerged, bringing together the best of these two areas to produce models closer to reality. As a result, distributions do not have easy mathematical solutions; manipulating uncertainties to obtain more realistic estimates and adopting techniques from other areas of knowledge strengthened the development of this new area for both Statistics and Computer Science. This book has the mission of bringing together the latest theoretical and practical developments of Computational Statistics, presenting examples of methodological evolution (for instance, with the use of techniques such as machine learning, intelligent methods, and parallel and statistical systems) and the combination of several techniques (forming hybrid analysis systems of statistical methods). This book also provides examples of applications of Computational Statistics methods across various areas of knowledge, from mathematical methods and statistical comparisons to engineering, medicine, economics, and materials science. The central idea of this book is to give the reader a view of the modern techniques of Computational Statistics that are being developed and applied, so that he can also use them to solve his problems, improving the representation of his models and creating new ways to simulate them and obtain results that are closer to reality.