This Element provides a detailed overview of the structural changes in the Asia-Pacific region from the early 2000s onwards. It reviews the most relevant literature on this important topic. The following two research areas are explored: first, by deploying visual network analysis (VNA), we analyse cross-border interbank claims and liabilities of the individual countries located in the Asia-Pacific region. Such an analysis evaluates interbank exposures to systematically important banks within the specific market. The important advantage of VNA is that it allows us to examine the 'hierarchical' cross-country interbank contagion risk that seems to have been neglected in similar studies. Secondly, we evaluate the contagion risk to the individual countries spreading from the financial centres in Hong Kong, Singapore, Tokyo, New York and London. The analysis unveils links and statistical factors that could be used as a key tool for detecting the potential triggers of systemic risk.
High-dimensional probability offers insight into the behavior of random vectors, random matrices, random subspaces, and objects used to quantify uncertainty in high dimensions. Drawing on ideas from probability, analysis, and geometry, it lends itself to applications in mathematics, statistics, theoretical computer science, signal processing, optimization, and more. It is the first to integrate theory, key tools, and modern applications of high-dimensional probability. Concentration inequalities form the core, and it covers both classical results such as Hoeffding's and Chernoff's inequalities and modern developments such as the matrix Bernstein's inequality. It then introduces the powerful methods based on stochastic processes, including such tools as Slepian's, Sudakov's, and Dudley's inequalities, as well as generic chaining and bounds based on VC dimension. A broad range of illustrations is embedded throughout, including classical and modern results for covariance estimation, clustering, networks, semidefinite programming, coding, dimension reduction, matrix completion, machine learning, compressed sensing, and sparse regression.
This textbook is a concise introduction to the basic toolbox of structures that allow efficient organization and retrieval of data, key algorithms for problems on graphs, and generic techniques for modeling, understanding, and solving algorithmic problems. The authors aim for a balance between simplicity and efficiency, between theory and practice, and between classical results and the forefront of research. Individual chapters cover arrays and linked lists, hash tables and associative arrays, sorting and selection, priority queues, sorted sequences, graph representation, graph traversal, shortest paths, minimum spanning trees, optimization, collective communication and computation, and load balancing. The authors also discuss important issues such as algorithm engineering, memory hierarchies, algorithm libraries, and certifying algorithms. Moving beyond the sequential algorithms and data structures of the earlier related title, this book takes into account the paradigm shift towards the parallel processing required to solve modern performance-critical applications and how this impacts on the teaching of algorithms. The book is suitable for undergraduate and graduate students and professionals familiar with programming and basic mathematical language. Most chapters have the same basic structure: the authors discuss a problem as it occurs in a real-life situation, they illustrate the most important applications, and then they introduce simple solutions as informally as possible and as formally as necessary so the reader really understands the issues at hand. As they move to more advanced and optional issues, their approach gradually leads to a more mathematical treatment, including theorems and proofs. The book includes many examples, pictures, informal explanations, and exercises, and the implementation notes introduce clean, efficient implementations in languages such as C++ and Java.
This graduate level textbook covers an especially broad range of topics. The book first offers a careful discussion of the basics of linear algebra. It then proceeds to a discussion of modules, emphasizing a comparison with vector spaces, and presents a thorough discussion of inner product spaces, eigenvalues, eigenvectors, and finite dimensional spectral theory, culminating in the finite dimensional spectral theorem for normal operators. The new edition has been revised and contains a chapter on the QR decomposition, singular values and pseudoinverses, and a chapter on convexity, separation and positive solutions to linear systems.
This Element provides a detailed overview of the structural changes in the Asia-Pacific region from the early 2000s onwards. It reviews the most relevant literature on this important topic. The following two research areas are explored: first, by deploying visual network analysis (VNA), we analyse cross-border interbank claims and liabilities of the individual countries located in the Asia-Pacific region. Such an analysis evaluates interbank exposures to systematically important banks within the specific market. The important advantage of VNA is that it allows us to examine the 'hierarchical' cross-country interbank contagion risk that seems to have been neglected in similar studies. Secondly, we evaluate the contagion risk to the individual countries spreading from the financial centres in Hong Kong, Singapore, Tokyo, New York and London. The analysis unveils links and statistical factors that could be used as a key tool for detecting the potential triggers of systemic risk.
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