Statistical Analysis Of Observations Of Increasing Dimension

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Statistical Analysis For High Dimensional Data

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Author : Arnoldo Frigessi
language : en
Publisher: Springer
Release Date : 2016-02-16

Statistical Analysis For High Dimensional Data written by Arnoldo Frigessi and has been published by Springer this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-02-16 with Mathematics categories.

This book features research contributions from The Abel Symposium on Statistical Analysis for High Dimensional Data, held in Nyvågar, Lofoten, Norway, in May 2014. The focus of the symposium was on statistical and machine learning methodologies specifically developed for inference in “big data” situations, with particular reference to genomic applications. The contributors, who are among the most prominent researchers on the theory of statistics for high dimensional inference, present new theories and methods, as well as challenging applications and computational solutions. Specific themes include, among others, variable selection and screening, penalised regression, sparsity, thresholding, low dimensional structures, computational challenges, non-convex situations, learning graphical models, sparse covariance and precision matrices, semi- and non-parametric formulations, multiple testing, classification, factor models, clustering, and preselection. Highlighting cutting-edge research and casting light on future research directions, the contributions will benefit graduate students and researchers in computational biology, statistics and the machine learning community.

An Introduction To Statistical Analysis Of Random Arrays

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Author : V. L. Girko
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2018-11-05

An Introduction To Statistical Analysis Of Random Arrays written by V. L. Girko and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2018-11-05 with Mathematics categories.

No detailed description available for "An Introduction to Statistical Analysis of Random Arrays".

Introduction To High Dimensional Statistics

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Author : Christophe Giraud
language : en
Publisher: CRC Press
Release Date : 2021-08-25

Introduction To High Dimensional Statistics written by Christophe Giraud and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-08-25 with Business & Economics categories.

Praise for the first edition: "[This book] succeeds singularly at providing a structured introduction to this active field of research. ... it is arguably the most accessible overview yet published of the mathematical ideas and principles that one needs to master to enter the field of high-dimensional statistics. ... recommended to anyone interested in the main results of current research in high-dimensional statistics as well as anyone interested in acquiring the core mathematical skills to enter this area of research." —Journal of the American Statistical Association Introduction to High-Dimensional Statistics, Second Edition preserves the philosophy of the first edition: to be a concise guide for students and researchers discovering the area and interested in the mathematics involved. The main concepts and ideas are presented in simple settings, avoiding thereby unessential technicalities. High-dimensional statistics is a fast-evolving field, and much progress has been made on a large variety of topics, providing new insights and methods. Offering a succinct presentation of the mathematical foundations of high-dimensional statistics, this new edition: Offers revised chapters from the previous edition, with the inclusion of many additional materials on some important topics, including compress sensing, estimation with convex constraints, the slope estimator, simultaneously low-rank and row-sparse linear regression, or aggregation of a continuous set of estimators. Introduces three new chapters on iterative algorithms, clustering, and minimax lower bounds. Provides enhanced appendices, minimax lower-bounds mainly with the addition of the Davis-Kahan perturbation bound and of two simple versions of the Hanson-Wright concentration inequality. Covers cutting-edge statistical methods including model selection, sparsity and the Lasso, iterative hard thresholding, aggregation, support vector machines, and learning theory. Provides detailed exercises at the end of every chapter with collaborative solutions on a wiki site. Illustrates concepts with simple but clear practical examples.

Computational Intelligent Data Analysis For Sustainable Development

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Author : Ting Yu
language : en
Publisher: CRC Press
Release Date : 2016-04-19

Computational Intelligent Data Analysis For Sustainable Development written by Ting Yu and has been published by CRC Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2016-04-19 with Business & Economics categories.

Going beyond performing simple analyses, researchers involved in the highly dynamic field of computational intelligent data analysis design algorithms that solve increasingly complex data problems in changing environments, including economic, environmental, and social data. Computational Intelligent Data Analysis for Sustainable Development presents novel methodologies for automatically processing these types of data to support rational decision making for sustainable development. Through numerous case studies and applications, it illustrates important data analysis methods, including mathematical optimization, machine learning, signal processing, and temporal and spatial analysis, for quantifying and describing sustainable development problems. With a focus on integrated sustainability analysis, the book presents a large-scale quadratic programming algorithm to expand high-resolution input-output tables from the national scale to the multinational scale to measure the carbon footprint of the entire trade supply chain. It also quantifies the error or dispersion between different reclassification and aggregation schemas, revealing that aggregation errors have a high concentration over specific regions and sectors. The book summarizes the latest contributions of the data analysis community to climate change research. A profuse amount of climate data of various types is available, providing a rich and fertile playground for future data mining and machine learning research. The book also pays special attention to several critical challenges in the science of climate extremes that are not handled by the current generation of climate models. It discusses potential conceptual and methodological directions to build a close integration between physical understanding, or physics-based modeling, and data-driven insights. The book then covers the conservation of species and ecologically valuable land. A case study on the Pennsylvania Dirt and Gravel Roads Program demonstrates that multiple-objective linear programming is a more versatile and efficient approach than the widely used benefit targeting selection process. Moving on to renewable energy and the need for smart grids, the book explores how the ongoing transformation to a sustainable energy system of renewable sources leads to a paradigm shift from demand-driven generation to generation-driven demand. It shows how to maximize renewable energy as electricity by building a supergrid or mixing renewable sources with demand management and storage. It also presents intelligent data analysis for real-time detection of disruptive events from power system frequency data collected using an existing Internet-based frequency monitoring network as well as evaluates a set of computationally intelligent techniques for long-term wind resource assessment. In addition, the book gives an example of how temporal and spatial data analysis tools are used to gather knowledge about behavioral data and address important social problems such as criminal offenses. It also applies constraint logic programming to a planning problem: the environmental and social impact assessment of the regional energy plan of the Emilia-Romagna region of Italy. Sustainable development problems, such as global warming, resource shortages, global species loss, and pollution, push researchers to create powerful data analysis approaches that analysts can then use to gain insight into these issues to support rational decision making. This volume shows both the data analysis and sustainable development communities how to use intelligent data analysis tools to address practical problems and encourages researchers to develop better methods.

Analysis Of Multivariate And High Dimensional Data

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Author : Inge Koch
language : en
Publisher: Cambridge University Press
Release Date : 2014

Analysis Of Multivariate And High Dimensional Data written by Inge Koch and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2014 with Business & Economics categories.

This modern approach integrates classical and contemporary methods, fusing theory and practice and bridging the gap to statistical learning.

Statistical Inference From High Dimensional Data

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Author : Carlos Fernandez-Lozano
language : en
Publisher: MDPI
Release Date : 2021-04-28

Statistical Inference From High Dimensional Data written by Carlos Fernandez-Lozano and has been published by MDPI this book supported file pdf, txt, epub, kindle and other format this book has been release on 2021-04-28 with Science categories.

• Real-world problems can be high-dimensional, complex, and noisy • More data does not imply more information • Different approaches deal with the so-called curse of dimensionality to reduce irrelevant information • A process with multidimensional information is not necessarily easy to interpret nor process • In some real-world applications, the number of elements of a class is clearly lower than the other. The models tend to assume that the importance of the analysis belongs to the majority class and this is not usually the truth • The analysis of complex diseases such as cancer are focused on more-than-one dimensional omic data • The increasing amount of data thanks to the reduction of cost of the high-throughput experiments opens up a new era for integrative data-driven approaches • Entropy-based approaches are of interest to reduce the dimensionality of high-dimensional data

Statistical Learning And Pattern Analysis For Image And Video Processing

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Author : Nanning Zheng
language : en
Publisher: Springer Science & Business Media
Release Date : 2009-07-25

Statistical Learning And Pattern Analysis For Image And Video Processing written by Nanning Zheng and has been published by Springer Science & Business Media this book supported file pdf, txt, epub, kindle and other format this book has been release on 2009-07-25 with Computers categories.

Why are We Writing This Book? Visual data (graphical, image, video, and visualized data) affect every aspect of modern society. The cheap collection, storage, and transmission of vast amounts of visual data have revolutionized the practice of science, technology, and business. Innovations from various disciplines have been developed and applied to the task of designing intelligent machines that can automatically detect and exploit useful regularities (patterns) in visual data. One such approach to machine intelligence is statistical learning and pattern analysis for visual data. Over the past two decades, rapid advances have been made throughout the ?eld of visual pattern analysis. Some fundamental problems, including perceptual gro- ing,imagesegmentation, stereomatching, objectdetectionandrecognition,and- tion analysis and visual tracking, have become hot research topics and test beds in multiple areas of specialization, including mathematics, neuron-biometry, and c- nition. A great diversity of models and algorithms stemming from these disciplines has been proposed. To address the issues of ill-posed problems and uncertainties in visual pattern modeling and computing, researchers have developed rich toolkits based on pattern analysis theory, harmonic analysis and partial differential eq- tions, geometry and group theory, graph matching, and graph grammars. Among these technologies involved in intelligent visual information processing, statistical learning and pattern analysis is undoubtedly the most popular and imp- tant approach, and it is also one of the most rapidly developing ?elds, with many achievements in recent years. Above all, it provides a unifying theoretical fra- work for intelligent visual information processing applications.

Multidimensional Statistical Analysis And Theory Of Random Matrices

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Author : A. K. Gupta
language : en
Publisher: Walter de Gruyter GmbH & Co KG
Release Date : 2019-01-14

Multidimensional Statistical Analysis And Theory Of Random Matrices written by A. K. Gupta and has been published by Walter de Gruyter GmbH & Co KG this book supported file pdf, txt, epub, kindle and other format this book has been release on 2019-01-14 with Mathematics categories.

No detailed description available for "Multidimensional Statistical Analysis and Theory of Random Matrices".

Robust Adaptive Beamforming

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Author : Jian Li
language : en
Publisher: John Wiley & Sons
Release Date : 2005-10-10

Robust Adaptive Beamforming written by Jian Li and has been published by John Wiley & Sons this book supported file pdf, txt, epub, kindle and other format this book has been release on 2005-10-10 with Technology & Engineering categories.

The latest research and developments in robust adaptivebeamforming Recent work has made great strides toward devising robust adaptivebeamformers that vastly improve signal strength against backgroundnoise and directional interference. This dynamic technology hasdiverse applications, including radar, sonar, acoustics, astronomy,seismology, communications, and medical imaging. There are alsoexciting emerging applications such as smart antennas for wirelesscommunications, handheld ultrasound imaging systems, anddirectional hearing aids. Robust Adaptive Beamforming compiles the theories and work ofleading researchers investigating various approaches in onecomprehensive volume. Unlike previous efforts, these pioneeringstudies are based on theories that use an uncertainty set of thearray steering vector. The researchers define their theories,explain their methodologies, and present their conclusions. Methodspresented include: * Coupling the standard Capon beamformers with a spherical orellipsoidal uncertainty set of the array steering vector * Diagonal loading for finite sample size beamforming * Mean-squared error beamforming for signal estimation * Constant modulus beamforming * Robust wideband beamforming using a steered adaptive beamformerto adapt the weight vector within a generalized sidelobe cancellerformulation Robust Adaptive Beamforming provides a truly up-to-date resourceand reference for engineers, researchers, and graduate students inthis promising, rapidly expanding field.

High Dimensional Data Analysis With Low Dimensional Models

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Author : John Wright
language : en
Publisher: Cambridge University Press
Release Date : 2022-01-13

High Dimensional Data Analysis With Low Dimensional Models written by John Wright and has been published by Cambridge University Press this book supported file pdf, txt, epub, kindle and other format this book has been release on 2022-01-13 with Computers categories.

Connects fundamental mathematical theory with real-world problems, through efficient and scalable optimization algorithms.