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Geometric Data Analysis

Author : Brigitte Le Roux
ISBN : 1402022360
Genre : Mathematics
File Size : 64. 92 MB
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Geometric Data Analysis (GDA) is the name suggested by P. Suppes (Stanford University) to designate the approach to Multivariate Statistics initiated by Benzécri as Correspondence Analysis, an approach that has become more and more used and appreciated over the years. This book presents the full formalization of GDA in terms of linear algebra - the most original and far-reaching consequential feature of the approach - and shows also how to integrate the standard statistical tools such as Analysis of Variance, including Bayesian methods. Chapter 9, Research Case Studies, is nearly a book in itself; it presents the methodology in action on three extensive applications, one for medicine, one from political science, and one from education (data borrowed from the Stanford computer-based Educational Program for Gifted Youth ). Thus the readership of the book concerns both mathematicians interested in the applications of mathematics, and researchers willing to master an exceptionally powerful approach of statistical data analysis.

Geometric Data Analysis

Author : Andreas Schmitz
ISBN : 1529750644
Genre : Political Science and International Relations
File Size : 21. 98 MB
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Geometric Data Analysis

Author : Michael Kirby
ISBN : 0471239291
Genre : Computers
File Size : 22. 6 MB
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This book addresses the most efficient methods of pattern analysis using wavelet decomposition. Readers will learn to analyze data in order to emphasize the differences between closely related patterns and then categorize them in a way that is useful to system users.

Combinatorial Inference In Geometric Data Analysis

Author : Brigitte Le Roux
ISBN : 9781498781626
Genre : Mathematics
File Size : 59. 85 MB
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Geometric Data Analysis designates the approach of Multivariate Statistics that conceptualizes the set of observations as a Euclidean cloud of points. Combinatorial Inference in Geometric Data Analysis gives an overview of multidimensional statistical inference methods applicable to clouds of points that make no assumption on the process of generating data or distributions, and that are not based on random modelling but on permutation procedures recasting in a combinatorial framework. It focuses particularly on the comparison of a group of observations to a reference population (combinatorial test) or to a reference value of a location parameter (geometric test), and on problems of homogeneity, that is the comparison of several groups for two basic designs. These methods involve the use of combinatorial procedures to build a reference set in which we place the data. The chosen test statistics lead to original extensions, such as the geometric interpretation of the observed level, and the construction of a compatibility region. Features: Defines precisely the object under study in the context of multidimensional procedures, that is clouds of points Presents combinatorial tests and related computations with R and Coheris SPAD software Includes four original case studies to illustrate application of the tests Includes necessary mathematical background to ensure it is self–contained This book is suitable for researchers and students of multivariate statistics, as well as applied researchers of various scientific disciplines. It could be used for a specialized course taught at either master or PhD level.

Riemannian Geometric Statistics In Medical Image Analysis

Author : Xavier Pennec
ISBN : 9780128147269
Genre : Computers
File Size : 46. 59 MB
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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. Beyond medical image computing, the methods described in this book may also apply to other domains such as signal processing, computer vision, geometric deep learning, and other domains where statistics on geometric features appear. As such, the presented core methodology takes its place in the field of geometric statistics, the statistical analysis of data being elements of nonlinear geometric spaces. The foundational material and the advanced techniques presented in the later parts of the book can be useful in domains outside medical imaging and present important applications of geometric statistics methodology Content includes: The foundations of Riemannian geometric methods for statistics on manifolds with emphasis on concepts rather than on proofs Applications of statistics on manifolds and shape spaces in medical image computing Diffeomorphic deformations and their applications As the methods described apply to domains such as signal processing (radar signal processing and brain computer interaction), computer vision (object and face recognition), and other domains where statistics of geometric features appear, this book is suitable for researchers and graduate students in medical imaging, engineering and computer science. A complete reference covering both the foundations and state-of-the-art methods Edited and authored by leading researchers in the field Contains theory, examples, applications, and algorithms Gives an overview of current research challenges and future applications

Data Depth

Author : Regina Y. Liu
ISBN : 0821871129
Genre : Mathematics
File Size : 50. 87 MB
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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 ofthe 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 theAssociation for Computer Machinery (ACM).

Ridges In Image And Data Analysis

Author : David Eberly
ISBN : 0792342682
Genre : Computers
File Size : 71. 52 MB
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The concept of ridges has appeared numerous times in the image processing liter ature. Sometimes the term is used in an intuitive sense. Other times a concrete definition is provided. In almost all cases the concept is used for very specific ap plications. When analyzing images or data sets, it is very natural for a scientist to measure critical behavior by considering maxima or minima of the data. These critical points are relatively easy to compute. Numerical packages always provide support for root finding or optimization, whether it be through bisection, Newton's method, conjugate gradient method, or other standard methods. It has not been natural for scientists to consider critical behavior in a higher-order sense. The con cept of ridge as a manifold of critical points is a natural extension of the concept of local maximum as an isolated critical point. However, almost no attention has been given to formalizing the concept. There is a need for a formal development. There is a need for understanding the computation issues that arise in the imple mentations. The purpose of this book is to address both needs by providing a formal mathematical foundation and a computational framework for ridges. The intended audience for this book includes anyone interested in exploring the use fulness of ridges in data analysis.

Stochastic Geometry

Author : Wilfrid S. Kendall
ISBN : 0849303966
Genre : Mathematics
File Size : 80. 95 MB
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Stochastic geometry involves the study of random geometric structures, and blends geometric, probabilistic, and statistical methods to provide powerful techniques for modeling and analysis. Recent developments in computational statistical analysis, particularly Markov chain Monte Carlo, have enormously extended the range of feasible applications. Stochastic Geometry: Likelihood and Computation provides a coordinated collection of chapters on important aspects of the rapidly developing field of stochastic geometry, including: o a "crash-course" introduction to key stochastic geometry themes o considerations of geometric sampling bias issues o tesselations o shape o random sets o image analysis o spectacular advances in likelihood-based inference now available to stochastic geometry through the techniques of Markov chain Monte Carlo

Efficient Structures For Geometric Data Management

Author : Oliver Günther
ISBN : 354050463X
Genre : Computers
File Size : 47. 77 MB
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More MRCP Part 1 provides five further mock MRCP type examination papers for quick self-assessment. It contains another collection of multiple choice questions used in the Bloomsbury MRCP Part 1 course and supplements the previously published volume: Johnson/Pozniak, MRCP Part 1. Any candidate preparing for such examinations will find valuable guidelines as to the strengths and weaknesses of his knowledge.

Fracman Version 2 3 Interactive Discrete Feature Data Analysis Geometric Modeling And Exploration Simulation

Author :
ISBN : OCLC:640848239
Genre :
File Size : 20. 6 MB
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Statistical Methods

Author : David J. Saville
ISBN : 0387947051
Genre : Mathematics
File Size : 85. 15 MB
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"[Suitable for] general interest readers as well as university students in their first or second year ... linear or vector geometry students who desire the illumination provided by a concrete application of the theory"--Page [1].

Multiple Correspondence Analysis

Author : Brigitte Le Roux
ISBN : 9781412968973
Genre : Mathematics
File Size : 82. 38 MB
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Requiring no prior knowledge of correspondence analysis, this text provides a nontechnical introduction to Multiple Correspondence Analysis (MCA) as a method in its own right. The authors, Brigitte LeRoux and Henry Rouanet, present thematerial in a practical manner, keeping the needs of researchers foremost in mind. Key Features Readers learn how to construct geometric spaces from relevant data, formulate questions of interest, and link statistical interpretation to geometric representations. They also learn how to perform structured data analysis and to draw inferential conclusions from MCA. The text uses real examples to help explain concepts. The authors stress the distinctive capacity of MCA to handle full-scale research studies. This supplementary text is appropriate for any graduate-level, intermediate, or advanced statistics course across the social and behavioral sciences, as well as for individual researchers. Learn more about “The Little Green Book” - QASS Series! Click Here

Statistical Methods And Safety Data Analysis And Evaluation

Author : National Research Council (U.S.). Transportation Research Board
ISBN : UVA:X004877134
Genre : Technology & Engineering
File Size : 62. 62 MB
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Data Analysis And Informatics Iii

Author : E. Diday
ISBN : 0444875557
Genre : Mathematics
File Size : 73. 21 MB
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Geometric Representations Of Relational Data

Author : James C. Lingoes
ISBN : UOM:39015000019706
Genre : Multidimensional scaling
File Size : 50. 1 MB
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Geometric Morphometrics For Biologists

Author : Miriam Leah Zelditch
ISBN : 9780123869043
Genre : Mathematics
File Size : 32. 29 MB
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The first edition of Geometric Morphometrics for Biologists has been the primary resource for teaching modern geometric methods of shape analysis to biologists who have a stronger background in biology than in multivariate statistics and matrix algebra. These geometric methods are appealing to biologists who approach the study of shape from a variety of perspectives, from clinical to evolutionary, because they incorporate the geometry of organisms throughout the data analysis. The second edition of this book retains the emphasis on accessible explanations, and the copious illustrations and examples of the first, updating the treatment of both theory and practice. The second edition represents the current state-of-the-art and adds new examples and summarizes recent literature, as well as provides an overview of new software and step-by-step guidance through details of carrying out the analyses. Contains updated coverage of methods, especially for sampling complex curves and 3D forms and a new chapter on applications of geometric morphometrics to forensics Offers a reorganization of chapters to streamline learning basic concepts Presents detailed instructions for conducting analyses with freely available, easy to use software Provides numerous illustrations, including graphical presentations of important theoretical concepts and demonstrations of alternative approaches to presenting results

Astronomical Data Analysis Software And Systems

Author :
ISBN : UCAL:$B448753
Genre : Astronomy
File Size : 72. 30 MB
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Geometric Topological Statistical Methods For The Analysis Of Image Data In R

Author : Taylor & Francis Group
ISBN : 1138322466
Genre :
File Size : 62. 5 MB
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Visualization And Data Analysis 2004

Author : Robert F. Erbacher
ISBN : 0819451983
Genre : Technology & Engineering
File Size : 38. 77 MB
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Proceedings of SPIE present the original research papers presented at SPIE conferences and other high-quality conferences in the broad-ranging fields of optics and photonics. These books provide prompt access to the latest innovations in research and technology in their respective fields. Proceedings of SPIE are among the most cited references in patent literature.

Data Analysis With Microsoft Excel

Author : Kenneth N. Berk
ISBN : PSU:000068297046
Genre : Microsoft Excel (Computer file)
File Size : 87. 13 MB
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