Modern Directional Statistics
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Author |
: Christophe Ley |
Publisher |
: CRC Press |
Total Pages |
: 233 |
Release |
: 2017-08-03 |
ISBN-10 |
: 9781351645782 |
ISBN-13 |
: 1351645781 |
Rating |
: 4/5 (82 Downloads) |
Synopsis Modern Directional Statistics by : Christophe Ley
Modern Directional Statistics collects important advances in methodology and theory for directional statistics over the last two decades. It provides a detailed overview and analysis of recent results that can help both researchers and practitioners. Knowledge of multivariate statistics eases the reading but is not mandatory. The field of directional statistics has received a lot of attention over the past two decades, due to new demands from domains such as life sciences or machine learning, to the availability of massive data sets requiring adapted statistical techniques, and to technological advances. This book covers important progresses in distribution theory,high-dimensional statistics, kernel density estimation, efficient inference on directional supports, and computational and graphical methods. Christophe Ley is professor of mathematical statistics at Ghent University. His research interests include semi-parametrically efficient inference, flexible modeling, directional statistics and the study of asymptotic approximations via Stein’s Method. His achievements include the Marie-Jeanne Laurent-Duhamel prize of the Société Française de Statistique and an elected membership at the International Statistical Institute. He is associate editor for the journals Computational Statistics & Data Analysis and Econometrics and Statistics. Thomas Verdebout is professor of mathematical statistics at Université libre de Bruxelles (ULB). His main research interests are semi-parametric statistics, high- dimensional statistics, directional statistics and rank-based procedures. He has won an annual prize of the Belgian Academy of Sciences and is an elected member of the International Statistical Institute. He is associate editor for the journals Statistics and Probability Letters and Journal of Multivariate Analysis.
Author |
: Christophe Ley |
Publisher |
: CRC Press |
Total Pages |
: 191 |
Release |
: 2017-08-03 |
ISBN-10 |
: 9781498706667 |
ISBN-13 |
: 1498706665 |
Rating |
: 4/5 (67 Downloads) |
Synopsis Modern Directional Statistics by : Christophe Ley
Modern Directional Statistics collects important advances in methodology and theory for directional statistics over the last two decades. It provides a detailed overview and analysis of recent results that can help both researchers and practitioners. Knowledge of multivariate statistics eases the reading but is not mandatory. The field of directional statistics has received a lot of attention over the past two decades, due to new demands from domains such as life sciences or machine learning, to the availability of massive data sets requiring adapted statistical techniques, and to technological advances. This book covers important progresses in distribution theory,high-dimensional statistics, kernel density estimation, efficient inference on directional supports, and computational and graphical methods. Christophe Ley is professor of mathematical statistics at Ghent University. His research interests include semi-parametrically efficient inference, flexible modeling, directional statistics and the study of asymptotic approximations via Stein’s Method. His achievements include the Marie-Jeanne Laurent-Duhamel prize of the Société Française de Statistique and an elected membership at the International Statistical Institute. He is associate editor for the journals Computational Statistics & Data Analysis and Econometrics and Statistics. Thomas Verdebout is professor of mathematical statistics at Université libre de Bruxelles (ULB). His main research interests are semi-parametric statistics, high- dimensional statistics, directional statistics and rank-based procedures. He has won an annual prize of the Belgian Academy of Sciences and is an elected member of the International Statistical Institute. He is associate editor for the journals Statistics and Probability Letters and Journal of Multivariate Analysis.
Author |
: Christophe Ley |
Publisher |
: CRC Press |
Total Pages |
: 400 |
Release |
: 2018-09-03 |
ISBN-10 |
: 9781351856522 |
ISBN-13 |
: 1351856529 |
Rating |
: 4/5 (22 Downloads) |
Synopsis Applied Directional Statistics by : Christophe Ley
This book collects important advances in methodology and data analysis for directional statistics. It is the companion book of the more theoretical treatment presented in Modern Directional Statistics (CRC Press, 2017). The field of directional statistics has received a lot of attention due to demands from disciplines such as life sciences or machine learning, the availability of massive data sets requiring adapted statistical techniques, and technological advances. This book covers important progress in bioinformatics, biology, astrophysics, oceanography, environmental sciences, earth sciences, machine learning and social sciences.
Author |
: Kanti V. Mardia |
Publisher |
: John Wiley & Sons |
Total Pages |
: 472 |
Release |
: 2000 |
ISBN-10 |
: UOM:39015053172394 |
ISBN-13 |
: |
Rating |
: 4/5 (94 Downloads) |
Synopsis Directional Statistics by : Kanti V. Mardia
Observations which are directions, axes or rotations occur in many sciences, including astronomy, biology, earth sciences, image analysis, and medicine. To analyse such data it is necessary to use the techniques of directional statistics, in which the special structure of circles, spheres and rotation groups is taken into account. This book gives a unified and comprehensive account of directional statistics, presenting both the underlying statistical theory and the practical methodology. The book is divided into three parts. The first part concentrates on statistics on the circle. Topics covered include tests of uniformity, tests of goodness-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 and curve fitting is presented. The third part considers statistics on more general sample spaces, in particular rotation groups, Stiefel manifolds, Grassmann manifolds and complex projective spaces. Shape analysis is considered from the perspective of directional statistics. This text will be invaluable not only to researchers in probability and statistics interested in the latest developments in directional statistics, but also to practitioners and researchers in many scientific fields, including astronomy, biology, computer vision, earth sciences and image analysis.
Author |
: N. I. Fisher |
Publisher |
: Cambridge University Press |
Total Pages |
: 300 |
Release |
: 1995-10-12 |
ISBN-10 |
: 0521568900 |
ISBN-13 |
: 9780521568906 |
Rating |
: 4/5 (00 Downloads) |
Synopsis Statistical Analysis of Circular Data by : N. I. Fisher
A unified, up-to-date account of circular data-handling techniques, useful throughout science.
Author |
: Arthur Pewsey |
Publisher |
: OUP Oxford |
Total Pages |
: 198 |
Release |
: 2013-09-26 |
ISBN-10 |
: 9780191650765 |
ISBN-13 |
: 0191650765 |
Rating |
: 4/5 (65 Downloads) |
Synopsis Circular Statistics in R by : Arthur Pewsey
Circular Statistics in R provides the most comprehensive guide to the analysis of circular data in over a decade. Circular data arise in many scientific contexts whether it be angular directions such as: observed compass directions of departure of radio-collared migratory birds from a release point; bond angles measured in different molecules; wind directions at different times of year at a wind farm; direction of stress-fractures in concrete bridge supports; longitudes of earthquake epicentres or seasonal and daily activity patterns, for example: data on the times of day at which animals are caught in a camera trap, or in 911 calls in New York, or in internet traffic; variation throughout the year in measles incidence, global energy requirements, TV viewing figures or injuries to athletes. The natural way of representing such data graphically is as points located around the circumference of a circle, hence their name. Importantly, circular variables are periodic in nature and the origin, or zero point, such as the beginning of a new year, is defined arbitrarily rather than necessarily emerging naturally from the system. This book will be of value both to those new to circular data analysis as well as those more familiar with the field. For beginners, the authors start by considering the fundamental graphical and numerical summaries used to represent circular data before introducing distributions that might be used to model them. They go on to discuss basic forms of inference such as point and interval estimation, as well as formal significance tests for hypotheses that will often be of scientific interest. When discussing model fitting, the authors advocate reduced reliance on the classical von Mises distribution; showcasing distributions that are capable of modelling features such as asymmetry and varying levels of kurtosis that are often exhibited by circular data. The use of likelihood-based and computer-intensive approaches to inference and modelling are stressed throughout the book. The R programming language is used to implement the methodology, particularly its "circular" package. Also provided are over 150 new functions for techniques not already covered within R. This concise but authoritative guide is accessible to the diverse range of scientists who have circular data to analyse and want to do so as easily and as effectively as possible.
Author |
: A V Dattatreya Rao |
Publisher |
: CRC Press |
Total Pages |
: 138 |
Release |
: 2019-12-20 |
ISBN-10 |
: 9781000739930 |
ISBN-13 |
: 1000739937 |
Rating |
: 4/5 (30 Downloads) |
Synopsis Angular Statistics by : A V Dattatreya Rao
Directional data arise in the form of circular / semicircular / axial, symmetric / asymmetric, uni / bimodal data, in practical situations of varied fields. For the purpose of modeling such kind of data sets, the data scientists found that existing models as inadequate. As there is paucity of angular models, and to fill the gap, this book is designed at constructing new angular models with the existing techniques and to develop new tools of constructing angular models with an application to control charts in angular models. This book is planned to cover the following topics in nine chapters Wrapped, stereographic and offset circular models Construction of angular models using Rising Sun function, positive definite sequences, discretization and through differential approach Extemporaneous Semicircular / arc and asymmetric l – axial models Choice of angular models as an inferential aspect and construction of control charts for angular data as an application are presented. This graduate level book will be useful for data scientists, researchers and research students of Statistics and allied fields.
Author |
: Ashis SenGupta |
Publisher |
: Springer Nature |
Total Pages |
: 487 |
Release |
: 2022-06-15 |
ISBN-10 |
: 9789811910449 |
ISBN-13 |
: 9811910448 |
Rating |
: 4/5 (49 Downloads) |
Synopsis Directional Statistics for Innovative Applications by : Ashis SenGupta
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.
Author |
: K. V. Mardia |
Publisher |
: Academic Press |
Total Pages |
: 380 |
Release |
: 2014-07-03 |
ISBN-10 |
: 9781483218663 |
ISBN-13 |
: 148321866X |
Rating |
: 4/5 (63 Downloads) |
Synopsis Statistics of Directional Data by : K. V. Mardia
Probability and Mathematical Statistics: A Series of Monographs and Textbooks: Statistics of Directional Data aims to provide a systematic account of statistical theory and methodology for observations which are directions. The publication first elaborates on angular data and frequency distributions, descriptive measures, and basic concepts and theoretical models. Discussions focus on moments and measures of location and dispersion, distribution function, corrections for grouping, calculation of the mean direction and the circular variance, interrelations between different units of angular measurement, and diagrammatical representation. The book then examines fundamental theorems and distribution theory, point estimation, and tests for samples from von Mises populations. The text takes a look at non-parametric tests, distributions on spheres, and inference problems on the sphere. Topics include tests for axial data, point estimation, distribution theory, moments and limiting distributions, and tests of goodness of fit and tests of uniformity. The publication is a dependable reference for researchers interested in probability and mathematical statistics.
Author |
: Regina Y. Liu |
Publisher |
: American Mathematical Soc. |
Total Pages |
: 264 |
Release |
: 2006 |
ISBN-10 |
: 9780821835968 |
ISBN-13 |
: 0821835963 |
Rating |
: 4/5 (68 Downloads) |
Synopsis Data Depth by : Regina Y. Liu
The book is a collection of some of the research presented at the workshop of the same name held in May 2003 at Rutgers University. The workshop brought together researchers from two different communities: statisticians and specialists in computational geometry. The main idea unifying these two research areas turned out to be the notion of data depth, which is an important notion both in statistics and in the study of efficiency of algorithms used in computational geometry. Many of the articles in the book lay down the foundations for further collaboration and interdisciplinary research. Information for our distributors: Co-published with the Center for Discrete Mathematics and Theoretical Computer Science beginning with Volume 8. Volumes 1-7 were co-published with the Association for Computer Machinery (ACM).