Topological And Statistical Methods For Complex Data

Author : Janine Bennett,Fabien Vivodtzev,Valerio Pascucci
Publisher : Springer
Page : 297 pages
File Size : 43,9 Mb
Release : 2014-11-19
Category : Mathematics
ISBN : 9783662449004

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Topological and Statistical Methods for Complex Data by Janine Bennett,Fabien Vivodtzev,Valerio Pascucci Pdf

This book contains papers presented at the Workshop on the Analysis of Large-scale, High-Dimensional, and Multi-Variate Data Using Topology and Statistics, held in Le Barp, France, June 2013. It features the work of some of the most prominent and recognized leaders in the field who examine challenges as well as detail solutions to the analysis of extreme scale data. The book presents new methods that leverage the mutual strengths of both topological and statistical techniques to support the management, analysis, and visualization of complex data. It covers both theory and application and provides readers with an overview of important key concepts and the latest research trends. Coverage in the book includes multi-variate and/or high-dimensional analysis techniques, feature-based statistical methods, combinatorial algorithms, scalable statistics algorithms, scalar and vector field topology, and multi-scale representations. In addition, the book details algorithms that are broadly applicable and can be used by application scientists to glean insight from a wide range of complex data sets.

Author : Anna Maria Paganoni,Piercesare Secchi
Publisher : Springer
Page : 210 pages
File Size : 50,5 Mb
Release : 2014-11-04
Category : Mathematics
ISBN : 9783319111490

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Advances in Complex Data Modeling and Computational Methods in Statistics by Anna Maria Paganoni,Piercesare Secchi Pdf

The book is addressed to statisticians working at the forefront of the statistical analysis of complex and high dimensional data and offers a wide variety of statistical models, computer intensive methods and applications: network inference from the analysis of high dimensional data; new developments for bootstrapping complex data; regression analysis for measuring the downsize reputational risk; statistical methods for research on the human genome dynamics; inference in non-euclidean settings and for shape data; Bayesian methods for reliability and the analysis of complex data; methodological issues in using administrative data for clinical and epidemiological research; regression models with differential regularization; geostatistical methods for mobility analysis through mobile phone data exploration. This volume is the result of a careful selection among the contributions presented at the conference "S.Co.2013: Complex data modeling and computationally intensive methods for estimation and prediction" held at the Politecnico di Milano, 2013. All the papers published here have been rigorously peer-reviewed.

Author : Hamish Carr,Christoph Garth,Tino Weinkauf
Publisher : Springer
Page : 363 pages
File Size : 45,5 Mb
Release : 2017-06-01
Category : Mathematics
ISBN : 9783319446844

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Topological Methods in Data Analysis and Visualization IV by Hamish Carr,Christoph Garth,Tino Weinkauf Pdf

This book presents contributions on topics ranging from novel applications of topological analysis for particular problems, through studies of the effectiveness of modern topological methods, algorithmic improvements on existing methods, and parallel computation of topological structures, all the way to mathematical topologies not previously applied to data analysis. Topological methods are broadly recognized as valuable tools for analyzing the ever-increasing flood of data generated by simulation or acquisition. This is particularly the case in scientific visualization, where the data sets have long since surpassed the ability of the human mind to absorb every single byte of data. The biannual TopoInVis workshop has supported researchers in this area for a decade, and continues to serve as a vital forum for the presentation and discussion of novel results in applications in the area, creating a platform to disseminate knowledge about such implementations throughout and beyond the community. The present volume, resulting from the 2015 TopoInVis workshop held in Annweiler, Germany, will appeal to researchers in the fields of scientific visualization and mathematics, domain scientists with an interest in advanced visualization methods, and developers of visualization software systems.

Author : Julien Tierny
Publisher : Springer
Page : 150 pages
File Size : 51,7 Mb
Release : 2018-01-16
Category : Mathematics
ISBN : 9783319715070

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Topological Data Analysis for Scientific Visualization by Julien Tierny Pdf

Combining theoretical and practical aspects of topology, this book provides a comprehensive and self-contained introduction to topological methods for the analysis and visualization of scientific data. Theoretical concepts are presented in a painstaking but intuitive manner, with numerous high-quality color illustrations. Key algorithms for the computation and simplification of topological data representations are described in detail, and their application is carefully demonstrated in a chapter dedicated to concrete use cases. With its fine balance between theory and practice, "Topological Data Analysis for Scientific Visualization" constitutes an appealing introduction to the increasingly important topic of topological data analysis for lecturers, students and researchers.

Author : Bernard Fichet,Domenico Piccolo,Rosanna Verde,Maurizio Vichi
Publisher : Springer Science & Business Media
Page : 460 pages
File Size : 45,7 Mb
Release : 2011-03-04
Category : Mathematics
ISBN : 9783642133121

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Classification and Multivariate Analysis for Complex Data Structures by Bernard Fichet,Domenico Piccolo,Rosanna Verde,Maurizio Vichi Pdf

The growing capabilities in generating and collecting data has risen an urgent need of new techniques and tools in order to analyze, classify and summarize statistical information, as well as to discover and characterize trends, and to automatically bag anomalies. This volume provides the latest advances in data analysis methods for multidimensional data which can present a complex structure: The book offers a selection of papers presented at the first Joint Meeting of the Société Francophone de Classification and the Classification and Data Analysis Group of the Italian Statistical Society. Special attention is paid to new methodological contributions from both the theoretical and the applicative point of views, in the fields of Clustering, Classification, Time Series Analysis, Multidimensional Data Analysis, Knowledge Discovery from Large Datasets, Spatial Statistics.

Author : Jean-Daniel Boissonnat,Frédéric Chazal,Mariette Yvinec
Publisher : Cambridge University Press
Page : 247 pages
File Size : 43,8 Mb
Release : 2018-09-27
Category : Computers
ISBN : 9781108419390

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Geometric and Topological Inference by Jean-Daniel Boissonnat,Frédéric Chazal,Mariette Yvinec Pdf

A rigorous introduction to geometric and topological inference, for anyone interested in a geometric approach to data science.

Author : Tamal Krishna Dey,Yusu Wang
Publisher : Cambridge University Press
Page : 455 pages
File Size : 45,7 Mb
Release : 2022-03-10
Category : Computers
ISBN : 9781009098168

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Computational Topology for Data Analysis by Tamal Krishna Dey,Yusu Wang Pdf

This book provides a computational and algorithmic foundation for techniques in topological data analysis, with examples and exercises.

Author : Germán Aneiros,Ivana Horová,Marie Hušková,Philippe Vieu
Publisher : Springer Nature
Page : 254 pages
File Size : 44,7 Mb
Release : 2020-06-19
Category : Mathematics
ISBN : 9783030477561

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Functional and High-Dimensional Statistics and Related Fields by Germán Aneiros,Ivana Horová,Marie Hušková,Philippe Vieu Pdf

This book presents the latest research on the statistical analysis of functional, high-dimensional and other complex data, addressing methodological and computational aspects, as well as real-world applications. It covers topics like classification, confidence bands, density estimation, depth, diagnostic tests, dimension reduction, estimation on manifolds, high- and infinite-dimensional statistics, inference on functional data, networks, operatorial statistics, prediction, regression, robustness, sequential learning, small-ball probability, smoothing, spatial data, testing, and topological object data analysis, and includes applications in automobile engineering, criminology, drawing recognition, economics, environmetrics, medicine, mobile phone data, spectrometrics and urban environments. The book gathers selected, refereed contributions presented at the Fifth International Workshop on Functional and Operatorial Statistics (IWFOS) in Brno, Czech Republic. The workshop was originally to be held on June 24-26, 2020, but had to be postponed as a consequence of the COVID-19 pandemic. Initiated by the Working Group on Functional and Operatorial Statistics at the University of Toulouse in 2008, the IWFOS workshops provide a forum to discuss the latest trends and advances in functional statistics and related fields, and foster the exchange of ideas and international collaboration in the field.

Author : Pierre Duchesne,Bruno Rémillard
Publisher : Springer
Page : 0 pages
File Size : 50,7 Mb
Release : 2010-10-29
Category : Mathematics
ISBN : 144193751X

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Statistical Modeling and Analysis for Complex Data Problems by Pierre Duchesne,Bruno Rémillard Pdf

This book reviews some of today’s more complex problems, and reflects some of the important research directions in the field. Twenty-nine authors – largely from Montreal’s GERAD Multi-University Research Center and who work in areas of theoretical statistics, applied statistics, probability theory, and stochastic processes – present survey chapters on various theoretical and applied problems of importance and interest to researchers and students across a number of academic domains.

Author : Nils A. Baas,Gunnar E. Carlsson,Gereon Quick,Markus Szymik,Marius Thaule
Publisher : Springer Nature
Page : 522 pages
File Size : 49,9 Mb
Release : 2020-06-25
Category : Mathematics
ISBN : 9783030434083

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Topological Data Analysis by Nils A. Baas,Gunnar E. Carlsson,Gereon Quick,Markus Szymik,Marius Thaule Pdf

This book gathers the proceedings of the 2018 Abel Symposium, which was held in Geiranger, Norway, on June 4-8, 2018. The symposium offered an overview of the emerging field of "Topological Data Analysis". This volume presents papers on various research directions, notably including applications in neuroscience, materials science, cancer biology, and immune response. Providing an essential snapshot of the status quo, it represents a valuable asset for practitioners and those considering entering the field.

Author : Kathryn Hess,Frédéric Chazal,Umberto Lupo
Publisher : Frontiers Media SA
Page : 229 pages
File Size : 50,9 Mb
Release : 2022-11-07
Category : Science
ISBN : 9782832504123

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Topology in Real-World Machine Learning and Data Analysis by Kathryn Hess,Frédéric Chazal,Umberto Lupo Pdf

Author : Agostino Di Ciaccio,Mauro Coli,Jose Miguel Angulo Ibanez
Publisher : Springer Science & Business Media
Page : 464 pages
File Size : 48,7 Mb
Release : 2012-03-05
Category : Mathematics
ISBN : 9783642210372

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Advanced Statistical Methods for the Analysis of Large Data-Sets by Agostino Di Ciaccio,Mauro Coli,Jose Miguel Angulo Ibanez Pdf

The theme of the meeting was “Statistical Methods for the Analysis of Large Data-Sets”. In recent years there has been increasing interest in this subject; in fact a huge quantity of information is often available but standard statistical techniques are usually not well suited to managing this kind of data. The conference serves as an important meeting point for European researchers working on this topic and a number of European statistical societies participated in the organization of the event. The book includes 45 papers from a selection of the 156 papers accepted for presentation and discussed at the conference on “Advanced Statistical Methods for the Analysis of Large Data-sets.”

Author : Gabor J. Szekely,Maria L. Rizzo
Publisher : CRC Press
Page : 444 pages
File Size : 49,7 Mb
Release : 2023-02-15
Category : Mathematics
ISBN : 9780429529269

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The Energy of Data and Distance Correlation by Gabor J. Szekely,Maria L. Rizzo Pdf

Energy distance is a statistical distance between the distributions of random vectors, which characterizes equality of distributions. The name energy derives from Newton's gravitational potential energy, and there is an elegant relation to the notion of potential energy between statistical observations. Energy statistics are functions of distances between statistical observations in metric spaces. The authors hope this book will spark the interest of most statisticians who so far have not explored E-statistics and would like to apply these new methods using R. The Energy of Data and Distance Correlation is intended for teachers and students looking for dedicated material on energy statistics, but can serve as a supplement to a wide range of courses and areas, such as Monte Carlo methods, U-statistics or V-statistics, measures of multivariate dependence, goodness-of-fit tests, nonparametric methods and distance based methods. •E-statistics provides powerful methods to deal with problems in multivariate inference and analysis. •Methods are implemented in R, and readers can immediately apply them using the freely available energy package for R. •The proposed book will provide an overview of the existing state-of-the-art in development of energy statistics and an overview of applications. •Background and literature review is valuable for anyone considering further research or application in energy statistics.

Author : Eric D. Kolaczyk,Gábor Csárdi
Publisher : Springer Nature
Page : 235 pages
File Size : 49,8 Mb
Release : 2020-06-02
Category : Computers
ISBN : 9783030441296

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Statistical Analysis of Network Data with R by Eric D. Kolaczyk,Gábor Csárdi Pdf

The new edition of this book provides an easily accessible introduction to the statistical analysis of network data using R. It has been fully revised and can be used as a stand-alone resource in which multiple R packages are used to illustrate how to conduct a wide range of network analyses, from basic manipulation and visualization, to summary and characterization, to modeling of network data. The central package is igraph, which provides extensive capabilities for studying network graphs in R. The new edition of this book includes an overhaul to recent changes in igraph. The material in this book is organized to flow from descriptive statistical methods to topics centered on modeling and inference with networks, with the latter separated into two sub-areas, corresponding first to the modeling and inference of networks themselves, and then, to processes on networks. The book begins by covering tools for the manipulation of network data. Next, it addresses visualization and characterization of networks. The book then examines mathematical and statistical network modeling. This is followed by a special case of network modeling wherein the network topology must be inferred. Network processes, both static and dynamic are addressed in the subsequent chapters. The book concludes by featuring chapters on network flows, dynamic networks, and networked experiments. Statistical Analysis of Network Data with R, 2nd Ed. has been written at a level aimed at graduate students and researchers in quantitative disciplines engaged in the statistical analysis of network data, although advanced undergraduates already comfortable with R should find the book fairly accessible as well.

Author : Raul Rabadan,Andrew J. Blumberg
Publisher : Cambridge University Press
Page : 522 pages
File Size : 51,8 Mb
Release : 2019-12-19
Category : Science
ISBN : 9781108757492

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Topological Data Analysis for Genomics and Evolution by Raul Rabadan,Andrew J. Blumberg Pdf

Biology has entered the age of Big Data. A technical revolution has transformed the field, and extracting meaningful information from large biological data sets is now a central methodological challenge. Algebraic topology is a well-established branch of pure mathematics that studies qualitative descriptors of the shape of geometric objects. It aims to reduce comparisons of shape to a comparison of algebraic invariants, such as numbers, which are typically easier to work with. Topological data analysis is a rapidly developing subfield that leverages the tools of algebraic topology to provide robust multiscale analysis of data sets. This book introduces the central ideas and techniques of topological data analysis and its specific applications to biology, including the evolution of viruses, bacteria and humans, genomics of cancer, and single cell characterization of developmental processes. Bridging two disciplines, the book is for researchers and graduate students in genomics and evolutionary biology as well as mathematicians interested in applied topology.