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Directional Statistics
Presents new and up-dated material on both the underlying theory and the practical methodology of directional statistics, helping the reader to utilise and develop the techniques appropriate to their work. The book is divided into three parts. The first part concentrates on statistics on the circle. Topics covered include tests of uniformity, tests of good-of-fit, inference on von Mises distributions and non-parametric methods. The second part considers statistics on spheres of arbitrary dimension, and includes a detailed account of inference on the main distributions on spheres. Recent material on correlation, regression time series, robust techniques, bootstrap methods, density estimation ...
Mo Jupp
None
The Sun from Space
The First Edition of The Sun from Space, completed in 1999, focused on the early accomplishments of three solar spacecraft, SOHO, Ulysses, and Yohkoh, primarily during a minimum in the Sun’s 11-year cycle of magnetic activity. The comp- hensive Second Edition includes the main ndings of these three spacecraft over an entire activity cycle, including two minima and a maximum, and discusses the signi cant results of six more solar missions. Four of these, the Hinode, RHESSI, STEREO, and TRACE missions were launched after the First Edition was either nished or nearly so, and the other two, the ACE and Wind spacecraft, extend our investigations from the Sun to its varying input to the Earth. The Second Edition does not contain simple updates or cosmetic patch ups to the material in the First Edition. It instead contains the relevant discoveries of the past decade, integrated into chapters completely rewritten for the purpose. This provides a fresh perspective to the major topics of solar enquiry, written in an enjoyable, easily understood text accessible to all readers, from the interested layperson to the student or professional.
Geometry Driven Statistics
A timely collection of advanced, original material in the area of statistical methodology motivated by geometric problems, dedicated to the influential work of Kanti V. Mardia This volume celebrates Kanti V. Mardia's long and influential career in statistics. A common theme unifying much of Mardia’s work is the importance of geometry in statistics, and to highlight the areas emphasized in his research this book brings together 16 contributions from high-profile researchers in the field. Geometry Driven Statistics covers a wide range of application areas including directional data, shape analysis, spatial data, climate science, fingerprints, image analysis, computer vision and bioinformatics. The book will appeal to statisticians and others with an interest in data motivated by geometric considerations. Summarizing the state of the art, examining some new developments and presenting a vision for the future, Geometry Driven Statistics will enable the reader to broaden knowledge of important research areas in statistics and gain a new appreciation of the work and influence of Kanti V. Mardia.
Flow
From its dynamic start at dance parties in the South Bronx in the late 1970s, hip hop and rap music have exploded into a dominant style of popular music in the United States and a force for activism and expression all over the world. So, too, has scholarship on hip hop and rap music grown. Yet much of this scholarship, employing methods drawn from sociology and literature, leaves unaddressed the expressive musical choices made by hip hop artists. Fundamental among these choices is the rhythm of the rapping voice, termed "flow." Flow presents unique theoretical and analytical challenges. It is rhythmic in the same way other music is rhythmic, but also in the way speech and poetry are rhythmic...
Econophysics and Financial Economics
This book provides the first extensive analytic comparison between models and results from econophysics and financial economics in an accessible and common vocabulary. Unlike other publications dedicated to econophysics, it situates this field in the evolution of financial economics by laying the foundations for common theoretical framework and models.
Riemannian Geometric Statistics in Medical Image Analysis
Over the past 15 years, there has been a growing need in the medical image computing community for principled methods to process nonlinear geometric data. Riemannian geometry has emerged as one of the most powerful mathematical and computational frameworks for analyzing such data. Riemannian Geometric Statistics in Medical Image Analysis is a complete reference on statistics on Riemannian manifolds and more general nonlinear spaces with applications in medical image analysis. It provides an introduction to the core methodology followed by a presentation of state-of-the-art methods. Content includes: - The foundations of Riemannian geometric methods for statistics on manifolds with emphasis o...
