modern directional statistics interdisciplinary statistics

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Modern Directional Statistics

Author : Christophe Ley
ISBN : 9781351645782
Genre : Computers
File Size : 78. 81 MB
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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.

Applied Directional Statistics

Author : Christophe Ley
ISBN : 1138626430
Genre :
File Size : 52. 48 MB
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This book describes new data-driven methodologies and case studies.The primary targeted audience is researchers in statistics, as well as those in fundamental roles such as earth sciences, environmetrics, astrophysics, machine learning, image analysis and bioinformatics.

Statistical Paradigms

Author : Ashis SenGupta
ISBN : 9789814644112
Genre : Mathematics
File Size : 46. 90 MB
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This volume consists of a collection of research articles on classical and emerging Statistical Paradigms — parametric, non-parametric and semi-parametric, frequentist and Bayesian — encompassing both theoretical advances and emerging applications in a variety of scientific disciplines. For advances in theory, the topics include: Bayesian Inference, Directional Data Analysis, Distribution Theory, Econometrics and Multiple Testing Procedures. The areas in emerging applications include: Bioinformatics, Factorial Experiments and Linear Models, Hotspot Geoinformatics and Reliability. Contents:Reviews:Weak Paradoxes and Paradigms (Jayanta K Ghosh)Nonparametrics in Modern Interdisciplinary Research: Some Perspectives and Prospectives (Pranab K Sen)Parametric:Bounds on Distributions Involving Partial, Marginal and Conditional Information: The Consequences of Incomplete Prior Specification (Barry C Arnold)Stepdown Procedures Controlling a Generalized False Discovery Rate (Wenge Guo and Sanat K Sarkar)On Confidence Intervals for Expected Response in 2n Factorial Experiments with Exponentially Distributed Response Variables (H V Kulkarni and S C Patil)Predictive Influence of Variables in a Linear Regression Model when the Moment Matrix is Singular (Md Nurul Haque Mollah and S K Bhattacharjee)New Wrapped Distributions — Goodness of Fit (A V Dattatreya Rao, I Ramabhadra Sarma and S V S Girija)Semi-Parametric:Non-Stationary Samples and Meta-Distribution (Dominique Guégan)MDL Model Selection Criterion for Mixed Models with an Application to Spline Smoothing (Antti Liski and Erkki P Liski)Digital Governance and Hotspot Geoinformatics with Continuous Fractional Response (G P Patil, S W Joshi and R E Koli)Bayesian Curve Registration of Functional Data (Z Zhong, A Majumdar and R L Eubank)Non-Parametric & Probability:Nonparametric Estimation in a One-Way Error Component Model: A Monte Carlo Analysis (Daniel J Henderson and Aman Ullah)GERT Analysis of Consecutive-k Systems: An Overview (Kanwar Sen, Manju Agarwal and Pooja Mohan)Moment Bounds for Strong-Mixing Processes with Applications (Ratan Dasgupta) Readership: Researchers, professionals and advanced students working on Bayesian and frequentist approaches to statistical modeling and on interfaces for both theory and applications. Key Features:A scholarly and motivating review of non-parametric methods by P K Sen, winner of the Wilks Medal in 2010Discussion of paradoxes of the frequentist and Bayesian paradigms, related counterexamples, and their implicationsStands out in terms of the width and depthKeywords:Bayesian Inference;Design of Experiments;Econometrics;Hotspot Geoinformatics;Linear Models and Regression Analysis;Multiple Testing Procedures;Probability Distributions for Linear and Directional Data;Reliability

Directional Statistics

Author : Kanti V. Mardia
ISBN : 9780470317815
Genre : Mathematics
File Size : 67. 5 MB
Format : PDF
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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 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. Written by leading authors in the field, 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.

Bayesian Methods In Structural Bioinformatics

Author : Thomas Hamelryck
ISBN : 9783642272257
Genre : Medical
File Size : 23. 45 MB
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This book is an edited volume, the goal of which is to provide an overview of the current state-of-the-art in statistical methods applied to problems in structural bioinformatics (and in particular protein structure prediction, simulation, experimental structure determination and analysis). It focuses on statistical methods that have a clear interpretation in the framework of statistical physics, rather than ad hoc, black box methods based on neural networks or support vector machines. In addition, the emphasis is on methods that deal with biomolecular structure in atomic detail. The book is highly accessible, and only assumes background knowledge on protein structure, with a minimum of mathematical knowledge. Therefore, the book includes introductory chapters that contain a solid introduction to key topics such as Bayesian statistics and concepts in machine learning and statistical physics.

Survival Analysis With Interval Censored Data

Author : Kris Bogaerts
ISBN : 9781420077483
Genre : Mathematics
File Size : 51. 20 MB
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Survival Analysis with Interval-Censored Data: A Practical Approach with Examples in R, SAS, and BUGS provides the reader with a practical introduction into the analysis of interval-censored survival times. Although many theoretical developments have appeared in the last fifty years, interval censoring is often ignored in practice. Many are unaware of the impact of inappropriately dealing with interval censoring. In addition, the necessary software is at times difficult to trace. This book fills in the gap between theory and practice. Features: -Provides an overview of frequentist as well as Bayesian methods. -Include a focus on practical aspects and applications. -Extensively illustrates the methods with examples using R, SAS, and BUGS. Full programs are available on a supplementary website. The authors: Kris Bogaerts is project manager at I-BioStat, KU Leuven. He received his PhD in science (statistics) at KU Leuven on the analysis of interval-censored data. He has gained expertise in a great variety of statistical topics with a focus on the design and analysis of clinical trials. Arnošt Komárek is associate professor of statistics at Charles University, Prague. His subject area of expertise covers mainly survival analysis with the emphasis on interval-censored data and classification based on longitudinal data. He is past chair of the Statistical Modelling Society?and editor of?Statistical Modelling: An International Journal. Emmanuel Lesaffre is professor of biostatistics at I-BioStat, KU Leuven. His research interests include Bayesian methods, longitudinal data analysis, statistical modelling, analysis of dental data, interval-censored data, misclassification issues, and clinical trials. He is the founding chair of the?Statistical Modelling Society, past-president of the?International Society for Clinical Biostatistics,?and fellow of?ISI?and?ASA.

Modern Interdisciplinary University Statistics Education

Author : Committee on Applied and Theoretical Statistics
ISBN : 9780309586382
Genre : Education
File Size : 50. 13 MB
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This book examines how the discipline of statistics should respond to the changing environment in which statisticians work. What does the academic, industry, and government customer need? How can the content of courses and of the overall statistics educational experience be arranged to address the customer's needs? Interdisciplinary needs are described, and successful university programs in interdisciplinary statistics are detailed.

Statistical Analysis Of Circular Data

Author : N. I. Fisher
ISBN : 0521568900
Genre : Mathematics
File Size : 70. 12 MB
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This book provides a unified and up-to-date account of techniques for handling circular data, and will interest all who perform data analyses.

Bayesian Disease Mapping

Author : Andrew B. Lawson
ISBN : 9781466504813
Genre : Mathematics
File Size : 82. 38 MB
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Since the publication of the first edition, many new Bayesian tools and methods have been developed for space-time data analysis, the predictive modeling of health outcomes, and other spatial biostatistical areas. Exploring these new developments, Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology, Second Edition provides an up-to-date, cohesive account of the full range of Bayesian disease mapping methods and applications. A biostatistics professor and WHO advisor, the author illustrates the use of Bayesian hierarchical modeling in the geographical analysis of disease through a range of real-world datasets. New to the Second Edition Three new chapters on regression and ecological analysis, putative hazard modeling, and disease map surveillance Expanded material on case event modeling and spatiotemporal analysis New and updated examples Two new appendices featuring examples of integrated nested Laplace approximation (INLA) and conditional autoregressive (CAR) models In addition to these new topics, the book covers more conventional areas such as relative risk estimation, clustering, spatial survival analysis, and longitudinal analysis. After an introduction to Bayesian inference, computation, and model assessment, the text focuses on important themes, including disease map reconstruction, cluster detection, regression and ecological analysis, putative hazard modeling, analysis of multiple scales and multiple diseases, spatial survival and longitudinal studies, spatiotemporal methods, and map surveillance. It shows how Bayesian disease mapping can yield significant insights into georeferenced health data. WinBUGS and R are used throughout for data manipulation and simulation.


Author : Gutti Jogesh Babu
ISBN : 0412983915
Genre : Mathematics
File Size : 41. 9 MB
Format : PDF, Kindle
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Modern astronomers encounter a vast range of challenging statistical problems, yet few are familiar with the wealth of techniques developed by statisticians. Conversely, few statisticians deal with the compelling problems confronted in astronomy. Astrostatistics bridges this gap. Authored by a statistician-astronomer team, it provides professionals and advanced students in both fields with exposure to issues of mutual interest. In the first half of the book the authors introduce statisticians to stellar, galactic, and cosmological astronomy and discuss the complex character of astronomical data. For astronomers, they introduce the statistical principles of nonparametrics, multivariate analysis, time series analysis, density estimation, and resampling methods. The second half of the book is organized by statistical topic. Each chapter contains examples of problems encountered astronomical research and highlights methodological issues. The final chapter explores some controversial issues in astronomy that have a strong statistical component. The authors provide an extensive bibliography and references to software for implementing statistical methods. The "marriage" of astronomy and statistics is a natural one and benefits both disciplines. Astronomers need the tools and methods of statistics to interpret the vast amount of data they generate, and the issues related to astronomical data pose intriguing challenges for statisticians. Astrostatistics paves the way to improved statistical analysis of astronomical data and provides a common ground for future collaboration between the two fields.

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