Spatial Data Analysis

This unique textbook provides an introduction to statistical inference with network data. The authors present a self-contained derivation and mathematical formulation of methods, review examples, and real-world applications, as well as provide data and code in the R environment that can be customised. Inferential network analysis transcends fields, and examples from across the social sciences are discussed (from management to electoral politics), which can be adapted and applied to a panorama of research. From scholars to undergraduates, spanning the social, mathematical, computational and physical sciences, readers will be introduced to inferential network models and their extensions. The exponential random graph model and latent space network model are paid particular attention and, fundamentally, the reader is given the tools to independently conduct their own analyses.

This unique textbook provides an introduction to statistical inference with network data. The authors present a self-contained derivation and mathematical formulation of methods, review examples, and real-world applications, as well as provide data and code in the R environment that can be customised. Inferential network analysis transcends fields, and examples from across the social sciences are discussed (from management to electoral politics), which can be adapted and applied to a panorama of research. From scholars to undergraduates, spanning the social, mathematical, computational and physical sciences, readers will be introduced to inferential network models and their extensions. The exponential random graph model and latent space network model are paid particular attention and, fundamentally, the reader is given the tools to independently conduct their own analyses.

This book serves as a current resource for Photoplethysmogram (PPG) signal analysis using MATLAB®. This technology is critical in the evaluation of medical and diagnostic data utilized in mobile devic

In the global race to reach the end of AIDS, why is the world slipping off track? The answer has to do with stigma, money, and data. Global funding for AIDS response is declining. Tough choices must be made: some people will win and some will lose. Global aid agencies and governments use health data to make these choices. While aid agencies prioritize a shrinking list of countries, many governments deny that sex workers, men who have sex with men, drug users, and transgender people exist. Since no data is gathered about their needs, life-saving services are not funded, and the lack of data reinforces the denial. The Uncounted cracks open this and other data paradoxes through interviews with global health leaders and activists, ethnographic research, analysis of gaps in mathematical models, and the author's experience as an activist and senior official. It shows what is counted, what is not, and why empowering communities to gather their own data could be key to ending AIDS.

Repeated measures data arise when the same characteristic is measured on each case or subject at several times or under several conditions. There is a multitude of techniques available for analysing

Data-driven computational neuroscience facilitates the transformation of data into insights into the structure and functions of the brain. This introduction for researchers and graduate students is the first in-depth, comprehensive treatment of statistical and machine learning methods for neuroscience. The methods are demonstrated through case studies of real problems to empower readers to build their own solutions. The book covers a wide variety of methods, including supervised classification with non-probabilistic models (nearest-neighbors, classification trees, rule induction, artificial neural networks and support vector machines) and probabilistic models (discriminant analysis, logistic regression and Bayesian network classifiers), meta-classifiers, multi-dimensional classifiers and feature subset selection methods. Other parts of the book are devoted to association discovery with probabilistic graphical models (Bayesian networks and Markov networks) and spatial statistics with po

Gives a comprehensive and systematic account of high-dimensional data analysis, including variable selection via regularization methods and sure independent feature screening methods. It is a valuable

Gives a comprehensive and systematic account of high-dimensional data analysis, including variable selection via regularization methods and sure independent feature screening methods. It is a valuable

Understand both uncoded and coded caching techniques in future wireless network design. Expert authors present new techniques that will help you to improve backhaul, load minimization, deployment cost reduction, security, energy efficiency and the quality of the user experience. Covering topics from high-level architectures to specific requirement-oriented caching design and analysis, including big-data enabled caching, caching in cloud-assisted 5G networks, and security, this is an essential resource for academic researchers, postgraduate students and engineers working in wireless communications.

In the global race to reach the end of AIDS, why is the world slipping off track? The answer has to do with stigma, money, and data. Global funding for AIDS response is declining. Tough choices must be made: some people will win and some will lose. Global aid agencies and governments use health data to make these choices. While aid agencies prioritize a shrinking list of countries, many governments deny that sex workers, men who have sex with men, drug users, and transgender people exist. Since no data is gathered about their needs, life-saving services are not funded, and the lack of data reinforces the denial. The Uncounted cracks open this and other data paradoxes through interviews with global health leaders and activists, ethnographic research, analysis of gaps in mathematical models, and the author's experience as an activist and senior official. It shows what is counted, what is not, and why empowering communities to gather their own data could be key to ending AIDS.

Analysing language data systematically and looking closely at how people formulate their thoughts can reveal astonishing insights about the human mind. Without presupposing specific subject knowledge, this book gently introduces its readers to theoretical insights as well as practical principles for systematic linguistic analysis from a cognitive perspective. Drawing on Thora Tenbrink's twenty years' experience in both linguistics and cognitive science, this book offers theoretical guidance and practical advice for doing cognitive discourse analysis. It covers areas of analysis as diverse as attention, perspective, granularity, certainty, inference, transformation, communication, and cognitive strategies, using inspiring examples from many different projects. Simple techniques and tools are used to allow readers new to the subject easy ways to apply the methods, without the need for complex technologies, whilst the cross-disciplinary approach can be applied to a diverse range of resear

Analysing language data systematically and looking closely at how people formulate their thoughts can reveal astonishing insights about the human mind. Without presupposing specific subject knowledge, this book gently introduces its readers to theoretical insights as well as practical principles for systematic linguistic analysis from a cognitive perspective. Drawing on Thora Tenbrink's twenty years' experience in both linguistics and cognitive science, this book offers theoretical guidance and practical advice for doing cognitive discourse analysis. It covers areas of analysis as diverse as attention, perspective, granularity, certainty, inference, transformation, communication, and cognitive strategies, using inspiring examples from many different projects. Simple techniques and tools are used to allow readers new to the subject easy ways to apply the methods, without the need for complex technologies, whilst the cross-disciplinary approach can be applied to a diverse range of resear

This book provides an introduction to the mathematical and algorithmic foundations of data science, including machine learning, high-dimensional geometry, and analysis of large networks. Topics include the counterintuitive nature of data in high dimensions, important linear algebraic techniques such as singular value decomposition, the theory of random walks and Markov chains, the fundamentals of and important algorithms for machine learning, algorithms and analysis for clustering, probabilistic models for large networks, representation learning including topic modelling and non-negative matrix factorization, wavelets and compressed sensing. Important probabilistic techniques are developed including the law of large numbers, tail inequalities, analysis of random projections, generalization guarantees in machine learning, and moment methods for analysis of phase transitions in large random graphs. Additionally, important structural and complexity measures are discussed such as matrix no

Already among the most important sectors of the US economy, the entertainment and media industries are continuing to grow worldwide. Fully updated, the tenth edition of Entertainment Industry Economics is the definitive reference on the economics of film, music, television, advertising, broadcasting, cable, casinos, publishing, arts and culture, performing arts, toys and games, sports, and theme parks. Its synthesis of a vast amount of data provides an up-to-date guide to the economics, financing, accounting, production, marketing, and history of these sectors in the United States and countries across the globe. This edition offers new material on streaming services, the relationship between demographics and entertainment spending, electromagnetic spectrum for broadcasters, and revised FASB accounting rules for film and television. Financial analysts and investors, economists, industry executives, accountants, lawyers, regulators, and journalists, as well as students preparing to join

Spectral analysis is widely used to interpret time series collected in diverse areas. This book covers the statistical theory behind spectral analysis and provides data analysts with the tools needed to transition theory into practice. Actual time series from oceanography, metrology, atmospheric science and other areas are used in running examples throughout, to allow clear comparison of how the various methods address questions of interest. All major nonparametric and parametric spectral analysis techniques are discussed, with emphasis on the multitaper method, both in its original formulation involving Slepian tapers and in a popular alternative using sinusoidal tapers. The authors take a unified approach to quantifying the bandwidth of different nonparametric spectral estimates. An extensive set of exercises allows readers to test their understanding of theory and practical analysis. The time series used as examples and R language code for recreating the analyses of the series are a

Learn about the state-of-the-art at the interface between information theory and data science with this first unified treatment of the subject. Written by leading experts in a clear, tutorial style, and using consistent notation and definitions throughout, it shows how information-theoretic methods are being used in data acquisition, data representation, data analysis, and statistics and machine learning. Coverage is broad, with chapters on signal acquisition, data compression, compressive sensing, data communication, representation learning, emerging topics in statistics, and much more. Each chapter includes a topic overview, definition of the key problems, emerging and open problems, and an extensive reference list, allowing readers to develop in-depth knowledge and understanding. Providing a thorough survey of the current research area and cutting-edge trends, this is essential reading for graduate students and researchers working in information theory, signal processing, machine le

Gathering Social Network Data provides an important complement to existing books that focus on social network analysis, and offers more detailed coverage than is available in existing chapter-length t

Symbolic data analysis is a relatively new field that provides a range of methods for analyzing complex datasets. Standard statistical methods do not have the power or flexibility to make sense of ver