Bøger af Ashis SenGupta

In commemoration of the bicentennial of the birth of the "e;lady who gave the rose diagram to us"e;, this special contributed book pays a statistical tribute to Florence Nightingale. This book presents recent phenomenal developments, both in rigorous theory as well as in emerging methods, for applications in directional statistics, in 25 chapters with contributions from 65 renowned researchers from 25 countries. With the advent of modern techniques in statistical paradigms and statistical machine learning, directional statistics has become an indispensable tool. Ranging from data on circles to that on the spheres, tori and cylinders, this book includes solutions to problems on exploratory data analysis, probability distributions on manifolds, maximum entropy, directional regression analysis, spatio-directional time series, optimal inference, simulation, statistical machine learning with big data, and more, with their innovative applications to emerging real-life problems in astro-statistics, bioinformatics, crystallography, optimal transport, statistical process control, and so on.

This book provides a comprehensive and rigorous treatment of real-life scientific problems which encounter non-linear data. The authors first present methods for developing distributions on a circle. Then, they proceed to show how such methods are generalized for other manifolds. They also consider new methods peculiar to certain other manifolds, like disc and hyperdisc. The organization of the book develops the methods from the beginning for a simple manifold, letting the reader appreciate how these unfold and generalize to more complicated manifolds. Next, rather than separately treating one distribution at a time, the authors develop the generalizations of the methods of derivations. Finally, new distributions are presented as outcomes of these generalizations. The authors also provide several real-life examples, which not only attest to the ongoing usefulness, but will also help the reader visualize other modern day areas of the applications of these important distributions.