Statistical Shape Analysis: With Applications in R

Author:   Ian L. Dryden (University of Nottingham) ,  Kanti V. Mardia (University of Leeds; University of Oxford, UK)
Publisher:   John Wiley & Sons Inc
Edition:   2nd edition
ISBN:  

9780470699621


Pages:   496
Publication Date:   16 September 2016
Format:   Hardback
Availability:   Out of stock   Availability explained
The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available.

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Statistical Shape Analysis: With Applications in R


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Overview

A thoroughly revised and updated edition of this introduction to modern statistical methods for shape analysis Shape analysis is an important tool in the many disciplines where objects are compared using geometrical features.  Examples include comparing brain shape in schizophrenia; investigating protein molecules in bioinformatics; and describing growth of organisms in biology. This book is a significant update of the highly-regarded Statistical Shape Analysis by the same authors. The new edition lays the foundations of landmark shape analysis, including geometrical concepts and statistical techniques, and extends to include analysis of curves, surfaces, images and other types of object data. Key definitions and concepts are discussed throughout, and the relative merits of different approaches are presented. The authors have included substantial new material on recent statistical developments and offer numerous examples throughout the text. Concepts are introduced in an accessible manner, while retaining sufficient detail for more specialist statisticians to appreciate the challenges and opportunities of this new field. Computer code has been included for instructional use, along with exercises to enable readers to implement the applications themselves in R and to follow the key ideas by hands-on analysis. Offers a detailed yet accessible treatment of statistical methods for shape analysis Includes numerous examples and applications from many disciplines Provides R code for implementing the examples Covers a wide variety of recent developments in shape analysis Shape Analysis, with Applications in R will offer a valuable introduction to this fast-moving research area for statisticians and other applied scientists working in diverse areas, including archaeology, bioinformatics, biology, chemistry, computer science, medicine, morphometics and image analysis.

Full Product Details

Author:   Ian L. Dryden (University of Nottingham) ,  Kanti V. Mardia (University of Leeds; University of Oxford, UK)
Publisher:   John Wiley & Sons Inc
Imprint:   John Wiley & Sons Inc
Edition:   2nd edition
Dimensions:   Width: 15.80cm , Height: 3.30cm , Length: 23.10cm
Weight:   0.862kg
ISBN:  

9780470699621


ISBN 10:   0470699620
Pages:   496
Publication Date:   16 September 2016
Audience:   College/higher education ,  Postgraduate, Research & Scholarly
Format:   Hardback
Publisher's Status:   Active
Availability:   Out of stock   Availability explained
The supplier is temporarily out of stock of this item. It will be ordered for you on backorder and shipped when it becomes available.

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Reviews

This is really an excellent, masterly, authoritative book about the statistical shape and size-and-shape analysis of landmark data. It provides the conceptual elements and then specific relationships and equations, working toward the various applications. The main results and equations are given in the text. In addition, there is a lot of information about how to use the tools...The book is well written with a well-integrated system of terms, notations, and derivations. Numerous elements on the historical background are provided. The reviewer highly recommends the reading of this book. (Mathematical Reviews/MathSciNet, July 2017) Statistical methods applied to shape analysis. Great for biologists, but strong mathematical treatment and accompanying code expands possible applications. (Raspberry Pi, March 2017)


Statistical methods applied to shape analysis. Great for biologists, but strong mathematical treatment and accompanying code expands possible applications. (Raspberry Pi March 2017)


Author Information

Ian Dryden, University of Nottingham, UK. Kanti Mardia, University of Leeds and University of Oxford, UK.

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