Directional Statistics for Innovative Applications: A Bicentennial Tribute to Florence Nightingale [Paperback]

In commemoration of the bicentennial of the birth of the lady who gave the rose diagram to us, 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.

Philippa M. Burdett, Kanti V. Mardia, Stuart Barber, John T. Kent and Thomas Hamelryck: Mixture Models for Spherical Data with Applications to Protein Bioinformatics.-Richard Arnold, Peter Jupp and Helmut Schaeben: Statistics of Orientation Relationships in Crystallography.- S. Rao Jammalamadaka, Gyorgy Terdik and Brian Wainwright: Simulation and Visualization of Spherical Distributions.- Jan Beran, Britta Steffens and Sucharita Ghosh: Some Applications of Long-range Dependence in Directional Data.- Barry C. Arnold and and Ashis SenGupta: Multivariate Power Cardioid Distributions on Hyper-Torus.- Peter Guttorp and Richard Lockhart: GLM Type Regression for Directional Data.- Andriette Bekker, Najmeh Nakhaei Rad, M. Arashi, Christophe Ley: Generalized Skew-Symmetric Circular and Toroidal Distributions.- Riccardo Gatto: Bimodal Spectra and the Generalised von Mises DistributionlC‚