Directional Statistics for Innovative Applications

A Bicentennial Tribute to Florence Nightingale
1. Auflage 2022
ISBN: 978-981-19-1043-2
Verlag: Springer Nature Singapore

Buch, Englisch, 488 Seiten, Format (B × H): 160 mm x 241 mm, Gewicht: 922 g

Reihe: Forum for Interdisciplinary Mathematics

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.

Zielgruppe

Research

Autoren/Hrsg.

Arnold, Barry C.

SenGupta, Ashis

Fachgebiete

• Mathematik | Informatik Mathematik Stochastik Mathematische Statistik

Inhaltsverzeichnis

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 Distribution.- Kunio Shimizu and Tomoaki Imoto: Circular Distribution Constructed from the Product of Cardioid-type Densitieswith (Hyper-) Toroidal Extension.- Toshihiro Abe, Tomoaki Imoto, Yoichi Miyat, Takayuki Shiohama: Recent Cylindrical Models and their Applications.- Xiaoping Zhan and Tiefeng Ma: A Complex Multiplication Regression Model for Circular Data.- Yogendra P. Chaubey: Nonparametric Density Estimation for Circular Data.- Fred Lombard, Douglas M. Hawkins and Cornelis J. Potgieter: SPC on a Circle: A Review and Some New Results.- S.H. Ong: Bivariate Cardioid Distributions.- Arnab K. Laha and Sourav Majumdar: Angular-Angular and Linear-Angular Regression Using ANN.- Hemangi V. Kulkarni: Efficient Estimation of Concentration Parameter of von Mises Distribution.- Shreyashi Basak, Kanika and Somesh Kumar: Robustness and Efficiency of Estimators for Mean Direction of a Wrapped Cauchy Distribution.- Sungsu Kim and Abeku A. Asare-Kumi: Diagnostic Analysis and Asymptotic Simultaneous Inference of the Three-Parameter Generalized von Mises Distribution.- Atanu Biswas and Jayant Jha: Regression Models forDirectional Data.- S.P. Mukherjee: Quality of Life: Florence Nightingale's Call for Improvement.- Francesco Lagona: Spatial Autoregressive Models for Circular Data.- Fidelis Ugwuowo: Models of Directional Time Series with Applications.- Kasirga Yildirak and Serdar Tugac: Wind Speed and Wind Direction Prediction by Deep Learning.- Malay Ghosh: Revisiting Wrapped Cauchy Distribution.- Axel Munk: To Receive.

Über Autor(innen)

Ashis SenGupta is Adjunct Professor at Augusta University, Georgia, USA; Distinguished Professor at Middle East Technical University, Turkey; and Advisor/Consultant at the Indian Statistical Institute, Kolkata, India. He completed his Ph.D. from Ohio State University, USA, and was visiting faculty at renowned universities across the world, including Stanford University, USA; University of California, Riverside, USA; University of California, Santa Barbara, USA; University of Wisconsin, Madison, USA; Michigan State University, East Lansing, USA; Concordia University, Montreal, Canada; Hebrew University of Jerusalem, Israel; Institute of Statistical Mathematics, Tokyo, Japan; Hacettepe University, Ankara, Turkey; and the University of Malaya, Malaysia. He is on the editorial board of the Forum for Interdisciplinary Mathematics (a Springer book series) and has been Editor-in-Chief of two international journals.               

Professor SenGupta has supervised the Ph.D. thesis of 14 scholars in India, Turkey and USA, and has authored more than 100 publications, including 12 books and edited volumes. His research interests include big data analytics, directional statistics, distribution theory, financial statistics, multivariate statistical analysis, reliability inference and statistical machine learning. He visited Mainland China as a Citizen Ambassador from the American Statistical Association and is the recipient of several international and national recognitions, including two lifetime achievements and one distinguished statistician awards. He is a member of several Expert, Project Advisory and Monitoring committees of the Department of Science and Technology, Government of India. He served as Vice-President of the Forum for Interdisciplinary Mathematics, USA; President (India chapter, 3 successive terms) of the International Indian Statistical Association, and President of the Mathematical Sciences section of the Indian Science Congress, and is recognized by International Statistical Institute, the Netherlands (Elected Member); Indian Society of Probability and Statistics, India (Fellow); National Academy of Sciences, India (Fellow); and American Statistical Association, USA (Fellow).

Barry C. Arnold is Distinguished Professor Emeritus at the Department of Statistics, University of California, Riverside, USA. He received his Ph.D. in Statistics from Stanford University, USA, in 1965. He has authored 14 books and more than 275 research papers in reputed peer-reviewed journals and contributed volumes. Professor Arnold has guided 17 Ph.D. scholars and has been on editorial boards of renowned journals. His research interests include estimation theory, probability, stochastic processes, mathematical learning models, biological models, characterizations, income distributions, order statistics, inequality measurement, record values, conditionally specified distributions, and Bayesian inference. He has been invited for scholarly lectures from across the world and is recognized by American Statistical Association, USA (Fellow); American Association for the Advancement of Science, USA (Fellow); Institute of Mathematical Statistics, USA (Fellow); Royal Statistical Society, UK (Fellow); International Statistical Institute, the Netherlands (Elected Member); and Forum for Interdisciplinary Mathematics, USA (Vice-President).